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  • What is Machine Learning? [2025 Updated]

    What is Machine Learning? [2025 Updated]

    What is Machine Learning?

    Want to make a career in the IT Industry with machine learning skill? Here is your chance to do that. In this amazing article, we will talk about “What is Machine Learning?” and how it can be beneficial for individuals and organizations working in the Industry.

    Moreover, we are going to mention a reputed training institute offering a dedicated training & certification program for machine learning skills. What are we waiting for? Let’s get straight to the topic!

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    Learn About Machine Learning?

    A branch of artificial intelligence called machine learning aims to make it possible for computers to learn from data without explicit programming. It uses algorithms that are able to recognize trends, forecast outcomes, and get better with time as they are given more data. Let’s talk about “What is Machine Learning?”

    What is Machine Learning?

    Why is Machine Learning Used?

    S.No. Factors Why?
    1. Automate tasks By automating time-consuming and repetitive processes, machine learning algorithms can free up human resources for more sophisticated work.
    2. Analyze large datasets In order to find hidden patterns and insights, machine learning (ML) can process and analyze enormous amounts of data that are impossible for people to handle effectively.
    3. Make predictions and forecasts ML models can forecast future trends, consumer behavior, and possible hazards by learning from past data, which helps in decision-making.
    4. Personalized experiences By analyzing user data, machine learning algorithms can offer tailored services, content, and suggestions that increase customer pleasure and engagement.
    5. Improve decision-making Data-driven insights from machine learning (ML) provide for more precise and well-informed decisions in a variety of domains, including business strategy, healthcare, and finance.
    6. Detect anomalies and fraud The ability of ML models to spot odd trends and outliers in data is essential for spotting security risks and fraudulent activity.
    7. Enable continuous improvement Over time, machine learning algorithms can improve their accuracy and performance by learning from fresh data and user feedback.
    8. Solve complex problems Image identification, natural language processing, autonomous systems, and other complex issues that are challenging to resolve with conventional rule-based programming can all be addressed via machine learning techniques.

    Different Types of Machine Learning

    The following are the different types of machine learning:

    1. Supervised Learning: To forecast results for fresh, unseen data, algorithms learn from labeled data.
    2. Unsupervised Learning: In unlabeled data, algorithms uncover hidden patterns and structures.
    3. Semi-Supervised Learning: Algorithms use both labeled and unlabeled data to learn.
    4. Reinforcement Learning: Through interactions with an environment and rewards or penalties, an agent learns how to behave optimally.
    5. Self-Supervised Learning: To learn representations, algorithms create their own labels from unlabeled input.

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    Advantages of Machine Learning

    S.No. Advantages How?
    1. Handles Complex Data Large and complex datasets would be too much for traditional analytical methods to handle, but machine learning algorithms can process them and extract valuable insights.
    2. Automation of Repetitive Tasks By automating repetitive processes like data entry, customer support queries, and preliminary analysis, machine learning (ML) can boost productivity and lower human error.
    3. Improved Decision-Making ML offers data-driven insights that result in more informed and strategic decisions by spotting patterns and trends in data.
    4. Personalized Experiences User happiness is increased by using machine learning (ML) to create personalized suggestions, information, and services based on each user’s unique behavior and interests.
    5. Predictive Capabilities Proactive planning is made possible by ML models’ ability to predict future events like sales patterns, probable equipment problems, and customer attrition.
    6. Anomaly and Fraud Detection Finding odd patterns and outliers in data is a strength of machine learning algorithms and is essential for spotting fraud and security lapses.
    7. Continuous Improvement Without deliberate retraining, machine learning (ML) systems can continuously improve their accuracy and performance by learning from new data and adapting over time.
    8. Scalability and Efficiency After being taught, machine learning models can frequently process fresh data and generate predictions far more quickly and extensively than human analysts.

    Disadvantages of Machine Learning

    The following are the disadvantages of machine learning:

    • Data Dependency: For machine learning algorithms to train efficiently, a lot of high-quality, pertinent data is usually needed, and performance can be severely harmed by inadequate or skewed data.
    • Computational Cost: Complex machine learning model training can be computationally costly and demand a large amount of time, energy, and processing power.
    • Lack of Transparency (Black Box): Interpretability and trust may be hampered by some complex machine learning models, particularly deep learning, which can be “black boxes,” meaning it’s hard to know why they produce particular predictions.
    • Potential for Bias: Biases in the training data may be learned and reinforced by the ML model, producing unfair or biased results.
    • Overfitting: Sometimes ML models perform poorly on fresh, unknown data because they learn the training data—including its noise—too well.
    • Maintenance and Updates: For machine learning models to remain accurate and adjust to shifting data patterns, they require constant observation, retraining, and updates.
    • Ethical Concerns: Privacy, security, accountability, and the possibility of abuse in domains such as autonomous weaponry and surveillance are among the ethical issues brought up by the application of machine learning.
    • Limited Generalization: Without extensive retraining or fine-tuning, models learned for a particular job or dataset might not generalize well to other workloads or datasets.

    How Does Supervised Machine Learning Work?

    In the following steps, the supervised machine learning works:

    1. Labeled Data: Supervised learning makes use of datasets in which every input is matched with an appropriate label or outcome.
    2. Training the Model: By examining the labeled training data, the algorithm discovers a mapping function between the inputs and outputs.
    3. Finding Patterns: The model finds underlying correlations and patterns in the input-output pairings while it is being trained.
    4. Creating a Model: A model that captures the relationships that have been learned is the end product of the training process.
    5. Making Predictions: The learnt patterns can then be used by the trained model to predict the related output from fresh, unknown input data.
    6. Evaluation: The accuracy and generalization capacity of the model are evaluated through the use of independent test data.
    7. Refinement (Optional): The parameters or architecture of the model may be changed to enhance its performance in light of the evaluation’s findings.

    How Does Unsupervised Machine Learning Work?

    S.No. Steps How?
    1. Unlabeled Data Datasets with no pre-assigned labels or goal outputs are fed into unsupervised learning methods. There are just input features in the data.
    2. Identifying Hidden Structures The main objective is for the algorithm to find correlations, structures, and patterns in the unlabeled data on its own.
    3. Feature Exploration Based on the attributes themselves, the algorithms examine the data to find patterns, distinctions, and clusters.
    4. Clustering Clustering is a typical task in which the algorithm, without prior knowledge of the categories, groups comparable data points together according to their intrinsic features.
    5. Dimensionality Reduction Reducing the dataset’s feature count while maintaining its structure and key information is another method. This can facilitate visualization and make further analysis easier.
    6. Association Rule Mining Finding intriguing correlations or links between various variables in the data is the goal of several unsupervised techniques.
    7. Model Creation Unsupervised learning frequently yields a collection of clusters, reduced dimensions, or association rules that show the data’s underlying structure.
    8. Interpretation Since there are no established labels to direct comprehension, humans usually interpret the patterns and structures that are found.

    How Does Semi-Supervised Learning Work?

    In the following ways, the Semi-Supervised Learning works:

    1. Combination of Labeled and Unlabeled Data: A dataset with both a significant amount of unlabeled data and a minor amount of labeled data is used in semi-supervised learning.
    2. Leveraging Unlabeled Data Structure: The algorithms try to understand the distribution and underlying structure of the whole dataset, including the unlabeled part.
    3. Initial Learning from Labeled Data: An initial signal for understanding the connection between inputs and outputs is provided by the labeled data.
    4. Exploiting Unlabeled Data: The knowledge gleaned from the labeled data is expanded and improved by the patterns found in the unlabeled data.
    5. Iterative Refinement: Based on its present knowledge, the model frequently iteratively creates pseudo-labels for the unlabeled data before retraining with the original labeled data and these pseudo-labels.
    6. Improving Generalization: Compared to strictly supervised learning with few labels, semi-supervised learning can frequently result in higher generalization performance on unseen data by utilizing the larger structure in the unlabeled data.
    7. Addressing Label Scarcity: When unlabeled data is easily accessible and labeled data is costly or time-consuming to get, this method is especially helpful.
    8. Enhanced Model Performance: Compared to using simply the labeled data, semi-supervised learning can greatly increase model accuracy when unlabeled data offers useful structural knowledge pertinent to the job.

    How Does Reinforcement Learning Work?

    S.No. Steps How?
    1. Agent and Environment An agent interacting with its surroundings is a component of reinforcement learning. The universe in which the agent functions is represented by the environment.
    2. Actions The agent can behave in its surroundings. As a result of these activities, the environment changes states.
    3. States The environment’s state denotes a particular setup or circumstance that the agent is in. These states are observed by the agent.
    4. Rewards The agent obtains a reward—positive or negative, from the environment after acting in a specific state. The reward indicates how desirable the action or state that resulted was.
    5. Policy The agent adheres to a policy, which is a plan that specifies what should be done in each situation. Finding the best course of action is the aim of reinforcement learning.
    6. Learning Through Trial and Error Through trial and error, the agent discovers the best course of action. It investigates various behaviors in various states and tracks the benefits that arise.
    7. Maximizing Cumulative Reward Learning a policy that maximizes the total reward the agent receives over time is its goal. This frequently entails striking a balance between short-term gains and long-term advantages.
    8. Value Functions (Optional) Value functions, which calculate the expected future reward for existing in a state or performing a specific action in a state, can be taught to the agent to aid in learning the best course of action.
    9. Iterative Process In order to enhance its performance over time, the agent continuously engages with the environment, acts, is rewarded, and modifies its policy (and maybe value functions).
    10. No Explicit Supervision There are no specifically labeled correct behaviors for every state, in contrast to supervised learning. The rewards are the only source of knowledge for the agent.

    Who Uses Machine Learning?

    Following are some of the entities that use machine learning:

    1. Technology Companies: For creating new AI-driven goods and services, boosting user interfaces, boosting recommendation systems, and refining search algorithms.
    2. E-commerce and Retail: Help manage inventory, anticipate consumer behavior, identify fraudulent transactions, improve pricing, and personalize product suggestions.
    3. Finance: For risk assessment, algorithmic trading, credit scoring, fraud detection, and individualized financial guidance.
    4. Healthcare: In the fields of medical image analysis, personalized medicine, medication development, diagnostics, and disease outbreak prediction.
    5. Transportation and Logistics: To enhance supply chain management, enable autonomous trucks, forecast arrival times, and optimize delivery routes.
    6. Manufacturing: For supply chain optimization, quality assurance, production process optimization, and equipment predictive maintenance.
    7. Marketing and Advertising: To forecast client attrition, optimize marketing expenditures, comprehend customer sentiment, and customize advertising strategies.
    8. Entertainment and Media: For targeted advertising, individualized user experiences, and content suggestions (movies, music, and articles).
    9. Security and Surveillance: To carry out facial recognition, find dangers, discover irregularities, and improve cybersecurity.
    10. Science and Research: Enabling data analysis, pattern detection, and speeding up discoveries in a variety of scientific domains, including materials science, astronomy, and genomics.

    Job Profiles After Learning Machine Learning Skills

    S.No. Job Profiles What?
    1. Machine Learning Engineer Creates, builds, and implements machine learning systems and models.
    2. Data Scientist Creates predictive models, analyzes intricate datasets, and shares insights gleaned from data.
    3. AI Engineer Emphasizes creating and deploying AI applications, frequently with machine learning.
    4. Research Scientist Research to create new algorithms and progress the science of machine learning.
    5. Data Analyst Uses machine learning (ML) techniques to extract and analyze data to find trends and offer practical recommendations.
    6. Natural Language Processing (NLP) Engineer Focuses on creating systems that are capable of processing and comprehending human language.
    7. Computer Vision Engineer Uses machine learning to make it possible for machines to “see” and comprehend pictures and movies.
    8. Robotics Engineer Uses machine learning to give robots the ability to sense their surroundings and make wise choices.
    9. Business Intelligence (BI) Developer Builds intelligent dashboards and reporting tools for corporate insights using machine learning.
    10. AI/ ML Product Manager Oversees the planning and development of products driven by AI and machine learning.

    Conclusion

    Now that we have talked about “What is Machine Learning?” you might be wondering where to get a reliable training provider for machine learning skills. For that, you can contact Craw Security, which offers a dedicated training & certification program, the Six-Month Diploma in Artificial Intelligence (AI) and Machine Learning for IT Aspirants.

    During the training sessions, students will try their skills on live machines via the virtual lab introduced on the premises of Craw Security under the guidance of experts. With that, online sessions offered by Craw Security will facilitate students’ remote learning.

    After the completion of the Six-Months Diploma in Artificial Intelligence (AI) and Machine Learning offered by Craw Security, students will receive a dedicated certificate validating their honed knowledge & skills during the sessions. What are you waiting for? Contact, Now!

    Frequently Asked Questions

    About What is Machine Learning?

    1. What is machine learning in simple terms?

    Teaching computers to learn from examples so they can make predictions or judgments without constant explicit instructions is what machine learning is all about.

    2. What is ML with an example?

    When a computer learns from data, it’s called machine learning. For example, Netflix uses your viewing history to recommend movies you might enjoy.

    3. What is ML, and types of ML?

    Computers can learn from data without explicit programming, thanks to the artificial intelligence discipline of machine learning. The following are the different types of ML:

    1. Supervised Learning,
    2. Unsupervised Learning,
    3. Semi-Supervised Learning,
    4. Reinforcement Learning, and
    5. Self-Supervised Learning.

    4. What’s the difference between AI and ML?

    Machine learning is a subfield of artificial intelligence (AI) that makes it possible for computers to learn from data, but AI as a whole is the larger field of building intelligent machines.

    5. Is ChatGPT AI or ML?

    An AI program called ChatGPT mostly uses machine learning, more specifically a transformer network, a kind of deep learning model.

    5. What is the purpose of machine learning?

    Machine learning aims to give computers the ability to learn from data, spot trends, and make judgments or predictions without the need for explicit programming.

    7. What is the full form of ML?

    Machine Learning is the full name of machine learning.

    8. How is machine learning used in cyber attacks?

    Machine Learning is used in cyberattacks in the following ways:

    1. AI-Driven Social Engineering,
    2. Malware Generation & Evasion,
    3. Network Traffic Analysis for Attacks,
    4. Automated Vulnerability Exploitation, and
    5. Adversarial Attacks on Security Systems.

    9. What are machine learning and ethical hacking?

    While ethical hacking is the legal practice of mimicking malevolent cyberattacks to find and address security flaws, machine learning is a branch of artificial intelligence that focuses on allowing systems to learn from data.

    10. What is an ML model in cybersecurity?

    An ML model is an algorithm used in cybersecurity that has been trained on security-related data to find trends, forecast threats, and automate security tasks such as anomaly or virus detection.

    11. What are AI and ML in cybersecurity?

    While machine learning (ML) is a crucial subset of artificial intelligence (AI), which allows systems to learn from data to enhance threat detection and response without explicit programming, artificial intelligence (AI) in cybersecurity refers to the larger field of developing intelligent systems to evaluate threats and automate defenses.

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  • Top 10 OWASP Risks in Mobile Application Security Testing [2024]

    Top 10 OWASP Risks in Mobile Application Security Testing [2024]

    Best 10 OWASP Risks in Mobile Application Security Testing

    The Open Web Application Security Project (OWASP) has meticulously identified the most critical risks in mobile application security. These risks can potentially expose sensitive data, compromise user privacy, and disrupt business operations. Understanding and addressing these risks is paramount for ensuring the security and integrity of mobile applications.

    1. Non-Secure Data Storage

    Many mobile apps store data locally on the device. If not stored securely, this data can be accessed by malicious apps or if the device gets compromised. Non-secure data storage often results from the use of plain-text files or unencrypted databases. Solutions include encrypting sensitive data before storage and using secure containers.

    2. Broken Cryptography

    This risk arises when mobile apps use weak algorithms or incorrect implementations for encryption. Even strong algorithms can be vulnerable if they are implemented incorrectly. To mitigate this risk, developers should use tried-and-tested cryptographic libraries and stay updated on cryptographic best practices.

    3. Weak Server-end Controls

    Many mobile apps interact with backend servers. If these servers have weak controls, they can become a gateway for attackers to access sensitive data. Regularly patching servers, implementing robust access controls, and conducting periodic security assessments are essential steps in reducing this risk.

    4. Inadequate Transport Layer Protection

    When data is transferred between the app and the server, it needs to be protected. Without adequate protection, data can be intercepted by malicious actors. Always use protocols like HTTPS and ensure proper SSL/TLS configurations.

    5. Unintentional Data Leakage

    Sometimes, mobile apps might leak sensitive data unintentionally due to issues like logging data in plain text or sharing data with third-party libraries without proper scrutiny. It’s vital to review the data handling processes and minimize data exposure.

    6. Client-Side Injection

    This occurs when an attacker can inject malicious code or commands from the client side. Examples include SQL injections or JavaScript injections. Developers should validate and sanitize all inputs and avoid using interpreters whenever possible.

    7. Lack of Binary Security

    If mobile app binaries are not protected, attackers can reverse engineer them to uncover vulnerabilities or sensitive information. Techniques like code obfuscation and tamper detection can enhance binary security.

    8. Poor Authorization and Authentication

    Without proper authorization and authentication mechanisms, malicious actors can gain unauthorized access to app functionalities. Implementing multi-factor authentication and ensuring role-based access control can be beneficial.

    9. Security Choices Through Non-Trusted Inputs

    When an application trusts inputs to make security decisions, it exposes itself to potential manipulation. Always validate and sanitize inputs, ensuring they don’t drive critical functionalities directly.

    10. Inappropriate Session Management

    Session management ensures that an authenticated user remains authenticated for a specific duration. Poor session management can allow attackers to hijack user sessions. Use secure and random session identifiers, and implement session timeouts.

    Best Practices for Mobile App Security Testing

    • Prioritize Risk Assessment: Conduct a comprehensive risk assessment to identify the most critical vulnerabilities based on your application’s functionality and data sensitivity.
    • Conduct Regular Penetration Testing: Employ automated and manual techniques to discover vulnerabilities and assess the effectiveness of your security measures.
    • Adhere to Secure Coding Practices: Follow established guidelines for writing secure code in your chosen programming language.
    • Leverage Static and Dynamic Analysis Tools: Automate the detection of vulnerabilities through static and dynamic analysis tools.
    • Stay Informed on Security Trends: Keep abreast of emerging threats and best practices in mobile application security.

    Conclusion

    Understanding the top risks associated with mobile application security is just the first step. Developers and businesses must remain vigilant, updating their knowledge and practices to stay ahead of evolving threats. Regular security testing, user education, and adopting a security-first approach in app development are the keys to building safer mobile applications for everyone.

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  • 10 Core Challenges in Mobile Application Testing

    10 Core Challenges in Mobile Application Testing

    Core Challenges in Mobile Application Testing

    Mobile application testing is essential for ensuring a high-quality user experience, and while this endeavor is crucial, it doesn’t come without its challenges. Below are ten core challenges testers often face while testing mobile apps.

    1. Device Fragmentation

    Mobile devices come in various models, makes, and versions. Android, in particular, is notorious for device fragmentation. Different devices often have unique hardware configurations, making it almost impossible to test an application on every single device. Testers need to prioritize based on market share and critical user segments.

    2. Different Screen Resolutions

    With devices ranging from small screen smartphones to large tablets, ensuring your app looks and functions consistently across varying screen resolutions is challenging. It’s crucial to ensure that design elements like buttons, images, and text render correctly on all screen sizes.

    3. Updated Device Models

    Every year, new device models are released with enhanced capabilities, higher resolutions, and different screen sizes. Testing must account for these innovations to ensure the app remains compatible with the latest and upcoming devices.

    4. Testing a Mobile App on Staging

    Before releasing an app, it’s essential to test it in a staging environment. This is a controlled environment that mimics the production server. Challenges arise when this staging environment doesn’t entirely replicate the live scenario, which can lead to inconsistencies when the app goes live.

    5. Mobile Network Bandwidth Issues

    Mobile apps can be accessed from anywhere, whether it’s a high-speed Wi-Fi network in an urban environment or a slow 2G network in a remote area. The challenge lies in ensuring the app performs consistently across various bandwidths, especially when handling large data transfers or streaming.

    6. Mobile App Security

    With rising cybersecurity threats, ensuring an app is secure is paramount. The app could be vulnerable to threats like data breaches, malware, or phishing attacks. Proper testing should validate that user data is encrypted and the app is free from vulnerabilities.

    7. Real User Condition Testing

    Simulating real-world conditions, like incoming calls or notifications while using the app, low battery conditions, or switching between apps, provides insights into how the app performs under such circumstances. These interruptions can cause apps to crash or behave unexpectedly if not tested properly.

    8. Different Types of Applications

    There are various types of apps – native, hybrid, and web apps. Each type has its unique testing requirements. For instance, a native app developed specifically for iOS will have different testing criteria compared to a hybrid app designed for both Android and iOS.

    9. Consistent User Experience

    Consistency in user experience is crucial for user retention. Users expect a smooth, intuitive experience irrespective of the device or platform they use. Testing needs to ensure that elements like app loading time, response time, and overall usability are consistent.

    10. Geolocation App Scenarios

    For apps that rely on geolocation services, testing becomes even more complicated. Factors like varying GPS strengths in different areas, or how the app behaves in different regions (considering regional content restrictions or variations) can all impact the user experience.

    Conclusion:
    Mobile app testing is an intricate process filled with numerous challenges. However, these challenges serve as reminders of the importance of thorough testing. By addressing each challenge head-on and employing the right testing strategies and tools, one can ensure that their app offers a consistent, secure, and high-quality experience for all users.

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  • How to Start a Career in AWS in 2023: A Comprehensive Guide

    How to Start a Career in AWS in 2023: A Comprehensive Guide

    In the rapidly evolving world of cloud computing, Amazon Web Services (AWS) has managed to establish a dominant position. As a result, there’s a growing demand for AWS professionals across a range of roles. But what if you’re a newcomer? Can you land an AWS job with no experience? Let’s dive in.

    Can You Get An AWS Job With No Experience in 2023?

    Absolutely! While experience can be beneficial, it’s not the only pathway into the AWS industry. Here are some steps to help you get started:

    1. Get Certified: AWS offers various certifications, ranging from entry-level to specialist. These certifications demonstrate your knowledge and proficiency in AWS.
    2. Build a Portfolio: Even without professional experience, you can work on personal projects or volunteer to get real-world exposure. Document these experiences in a portfolio.
    3. Network: Connect with professionals in the industry, attend AWS events, or join AWS-related online communities.
    4. Be Persistent: Consistency is key. Keep applying, enhancing your skills, and stay updated with AWS trends.

    Jobs In AWS Without Experience

    If you’re starting with no experience, consider these roles to kick-start your AWS journey:

    • Internships: Many companies offer internships for fresh graduates or those looking to shift careers.
    • Entry-Level Jobs: Some roles may require foundational knowledge but not necessarily years of experience.
    • AWS Certified Cloud Practitioner: This entry-level certification can open doors to foundational cloud roles.
    • Start-ups and Small Companies: They often look for passion and potential rather than just experience.
    • Freelance Projects: Freelancing platforms offer AWS projects that can help you gain practical exposure.
    • AWS Training Programs: These programs can provide hands-on learning and sometimes direct placement opportunities.

    Why Choose A Career in AWS?

    • High Demand: With more companies moving to the cloud, AWS professionals are in high demand.
    • Versatility: From developers to architects to system ops, AWS offers varied career paths.
    • Competitive Salaries: AWS professionals command attractive salaries, given the specialized skills they bring.

    How To Learn AWS?

    1. Understand the Basics: Familiarize yourself with cloud concepts and AWS’s service offerings.
    2. Online Courses: Platforms like Coursera, Udemy, and AWS’s own training provide comprehensive courses.
    3. Read AWS Documentation: AWS’s official documentation is a goldmine of information.
    4. AWS Certifications: Aim for certifications to validate your knowledge.
    5. Hands-On Practice: Use AWS’s free tier to get practical exposure.
    6. Build Projects: Create real-world applications using AWS services.
    7. Read AWS Whitepapers: They offer in-depth knowledge on specific topics.
    8. Participate in AWS Events and Webinars: Engage with experts and keep up with the latest trends.
    9. Use Q&A platforms: Websites like StackOverflow provide answers to AWS-related queries.
    10. Stay Updated: AWS constantly evolves, so subscribing to AWS blogs or news channels can be beneficial.

    How to Start an AWS Career?

    • Assess Your Background: Identify if you already have skills that can be relevant, like programming or database management.
    • Choose a Learning Path: Based on your interest, focus on areas like development, architecture, or system operations.
    • Certification: Begin with the AWS Cloud Practitioner and then proceed to more specialized certifications.
    • Practical Exposure: Build projects, contribute to open-source, or even intern to gain real-world exposure.

    What is the AWS Certificate?

    AWS Certifications validate your cloud expertise. They range from foundational certifications like the AWS Certified Cloud Practitioner to more specialized ones like AWS Certified Solutions Architect, AWS Certified Developer, and more.

    What is the average salary of AWS employees?

    Salaries vary based on the role, location, and experience. However, as of my last training cut-off in September 2021, AWS-certified professionals often command salaries above the industry average. For specific figures, it would be best to refer to recent salary surveys or websites like Glassdoor and Payscale.

    Conclusion

    Starting a career in AWS in 2023, even without experience, is attainable. With the right strategy, commitment to learning, and networking, you can carve out a lucrative and fulfilling career in the AWS domain. Embrace the journey, stay persistent, and the cloud’s the limit!

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  • Penetration Testing: An Essential Guide to Cybersecurity (2025)

    Penetration Testing: An Essential Guide to Cybersecurity (2025)

    Penetration Testing: An Essential Guide to Cybersecurity (2025)

    Penetration testing, often called pen testing or ethical hacking, involves simulating cyberattacks to evaluate the security of systems, networks, or applications. The primary goal of penetration testing is to identify vulnerabilities and weaknesses that real attackers might exploit.

    What Is Penetration Testing?

    Penetration testing is a cybersecurity practice designed to identify, test, and highlight vulnerabilities. Pen tests simulate cyberattacks on your computer systems, applications, and networks to uncover exploitable vulnerabilities and ensure robust cybersecurity.

    What is Pentesting at craw security

    What are The Types of Penetration Testing?

    Penetration testing covers several specialized approaches. Below are the main types of penetration tests:

    1. External Penetration Testing
    External tests target internet-visible assets like websites, email servers, DNS, and external network servers. The aim is to identify how attackers could access and extract valuable data.

    2. Internal Penetration Testing
    Internal tests simulate insider threats, assessing potential vulnerabilities from users with initial access within the network.

    3. Blind Penetration Testing
    Testers receive only minimal information (usually just the organization’s name), replicating a realistic external attack scenario.

    4. Double-Blind Penetration Testing
    Both security teams and testers have no prior knowledge, effectively evaluating real-time security responses and incident management processes.

    5. Targeted (Lights-On) Penetration Testing
    A collaborative testing method where both the testers and security teams are aware of the testing. Often used as a training exercise for cybersecurity teams.

    6. Social Engineering Testing
    Involves attempts to manipulate staff into breaking security procedures via phishing, pretexting, baiting, quid pro quo, or tailgating.

    7. Physical Penetration Testing
    Evaluates physical security measures by attempting unauthorized access to sensitive locations such as data centers or secure servers.

    8. Wireless Penetration Testing
    Identifies vulnerabilities in wireless networks, including Wi-Fi systems, to prevent unauthorized access.

    9. Application Penetration Testing
    Focuses on vulnerabilities in web applications, mobile applications, or desktop applications, covering coding practices, insecure features, and security gaps.

    10. Cloud Penetration Testing
    Targets vulnerabilities in cloud infrastructures such as AWS, Azure, or Google Cloud, addressing specific cloud security challenges.

     

    How Is Penetration Testing Conducted? The Penetration Testing Process

    A standard penetration test follows a structured five-step methodology:

    1. Planning: Define scope, objectives, and testing methodologies.
    2. Reconnaissance: Collect extensive information about target systems.
    3. Attack/Exploitation: Execute controlled attacks to exploit vulnerabilities.
    4. Maintaining Access: Evaluate possibilities for ongoing vulnerability exploitation.

     

    After Penetration Testing: Next Steps and Best Practices

    Once penetration testing concludes, prioritize findings, patch vulnerabilities, and conduct retests to ensure all identified weaknesses have been resolved.

     

    Frequently Asked Questions (FAQs) about Penetration Testing

    Q1: What are the five stages of penetration testing?
    The five stages include Planning, Reconnaissance, Attack, Maintaining Access, and Reporting.

    Q2: What is penetration testing? Can you provide an example?
    Penetration testing identifies cybersecurity vulnerabilities. For example, a bank may hire ethical hackers to test an online banking system, revealing if unauthorized access to customer accounts is possible.

    Q3: What type of testing is penetration testing?
    Penetration testing is a type of security testing focused specifically on discovering vulnerabilities and potential exploits.

    Q4: What is penetration testing in QA?
    In quality assurance (QA), penetration testing ensures applications or systems are secure from cyberattacks, emphasizing overall security quality.

    Q5: Why is it called a penetration test?
    It’s called a “penetration test” because testers attempt to “penetrate” or breach security defenses.

    Q6: Why use penetration testing tools?
    Penetration testing tools automate complex tasks, identify vulnerabilities faster, and enhance efficiency and accuracy.

    Q7: Who typically performs penetration testing?
    Professional ethical hackers, cybersecurity firms, or in-house security teams perform penetration tests.

    Q8: What tools are used in penetration testing?
    Common penetration testing tools include Metasploit, Nmap, Wireshark, Burp Suite, Nessus, and many others, depending on test requirements.

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  • How to Become a Computer Forensics Investigator? [2025]

    How to Become a Computer Forensics Investigator? [2025]

    How to Become a Computer Forensics Investigator?

    The fascinating elements of law enforcement and the technological know-how of an IT specialist are combined in the profession of computer forensic investigation.  This thorough guide on how to become a computer forensic investigator is ideal for anyone who wants to work in this dynamic field.

    What is a Computer Forensic Investigator?

    Learn about What is a Computer Forensic Investigator?

    To support criminal investigations, a computer forensic investigator, also known as a digital forensics analyst, looks into digital data.  In order to use it as evidence in criminal investigations, they are responsible for retrieving and analyzing data from computers and other digital devices.  Prerequisites for this profession include a strong foundation in computer science, a keen analytical mind, and a thorough understanding of legal procedures.

    Types of Digital Forensic Investigation

    There are several areas of digital forensic investigations, each focusing on different aspects of digital devices and data.  Some common categories are as follows:

    Computer Forensics Involves getting data from computers and storage devices and looking it over. In cases involving fraud, hacking, and cybercrimes, it is essential.
    Network Forensics In order to detect and prevent network breaches or attacks, the main goal is to monitor and assess network activity, including both local and wide area networks.
    Mobile Device Forensics It emphasizes data analysis and retrieval from mobile devices, including tablets and smartphones. The rise in mobile computing has made this extremely important.
    Cloud Forensics Involves looking at data that is kept in cloud computing environments. As more and more people use cloud services for apps and data storage, the sector is growing.
    Forensic Data Analysis Analyzing large datasets to find trends, fraudulent activity, or other relevant data is part of the broad field of data analytics.
    Database Forensics Investigating and retrieving data from databases, including examining logs, data structures, and raw database files, is the main focus of this field of research.
    Malware Forensics The discipline entails locating and analyzing malicious software to understand its traits, origin, and any effects on the system.
    Email Forensics In order to find evidence of email fraud, phishing attacks, or other security breaches, email forensics includes the retrieval and analysis of emails, including those that have been erased.
    Memory Forensics Involves closely examining the transient data kept in a computer’s random access memory (RAM) in order to uncover possible evidence that might not be found in the hard drive’s data.

    What Does a Computer Forensic Investigator Do?

    In order to find digital evidence for court cases and security breaches, a computer forensic investigator is essential.  Their main responsibilities include:

    Evidence Collection The work entails locating and obtaining digital evidence from multiple sources while preserving its integrity and keeping an accurate record of its ownership.
    Data Analysis Examining digital data to uncover relevant details. Examining file systems, recovering erased files, and decrypting encrypted data are all included in this.
    Investigating Cybercrimes Addressing issues related to fraud, cyberstalking, data breaches, hacking, and other cybercrimes.
    Report Writing Putting together thorough reports on the results that can be used in court. This involves communicating complex technical information in a way that non-technical people can understand.
    Testifying in Court Serving in court as a professional witness and explaining the findings and procedures used in the evidence gathering process.
    Staying Updated Keeping up with the latest developments in forensic software, techniques, and technology.
    Adhering to Legal and Ethical Standards Ensuring that inquiries follow the law and moral principles.
    Interdisciplinary Collaboration Coordinating with legal teams, IT specialists, and law enforcement to ensure a comprehensive approach to digital investigations.

    Career Scope of Computer Forensics in 2025

    Learn about career scope of computer forensics in 2025

    The increasing reliance on digital technology and the internet in both public and private spheres has led to a significant growth in the field of computer forensics.  For anyone interested in a career in this field, the expansion offers a wide range of professional opportunities and a positive outlook.  A more thorough analysis of the various job options is provided below:

    • Diverse Industries,
    • Growing Demand in Law Enforcement,
    • Private Sector Opportunities,
    • Consultancy Roles,
    • Educational and Research Opportunities,
    • Global Opportunities,
    • Advancement Prospects, etc.

    Is Computer Forensics worth it?

    A person’s personal interests and career goals will determine how worthwhile it is to pursue a career in computer forensics.  However, there are more factors that add to its allure:

    • Growing Relevance,
    • Intellectually Stimulating Work,
    • Positive Job Outlook,
    • Competitive Salaries,
    • Making a Difference,
    • Interdisciplinary Nature,
    • Dynamic and Evolving Field, and many more.

    How to Become a Computer Forensic Investigator?

    One needs a mix of formal education, technological know-how, and practical experience to work as a computer forensic investigator. The necessary steps to enter this field are listed below:

    Educational Background Obtain an undergraduate degree in criminal justice, cybersecurity, computer science, or a closely related field. This provides a strong foundation in the fundamental legal and technical aspects.  Additionally, Craw Security offers a one-year diploma program in cybersecurity powered by artificial intelligence.
    Gain Technical Proficiency Gain proficiency in areas like data retrieval, cryptography, and network security. It is crucial to be proficient with forensic software tools.
    Certifications Obtain relevant certifications, such as the Diploma in Cyber Forensics by Craw Security, the Cyber Forensics Investigation Course by Craw Security, and the EC-Council CHFI v11.
    Practical Experience Participate in internships or begin at the entry level in the cybersecurity or IT industries to gain real-world experience. This real-world scenario provides invaluable practical experience.
    Specialized Training It is recommended that you seek specialist training or take classes on digital forensics to increase your expertise in this area.
    Stay Updated To keep abreast of the rapidly evolving technologies and trends in cybercrime, make sure you continuously expand your knowledge.
    Networking and Professional Development To build a solid professional network and keep abreast of the most recent developments in the business, become involved in workshops, seminars, and professional groups.

    Five Steps in the Computer Forensics Process

    Learn about Five Steps in the Computer Forensics Process

    The following steps are frequently included in the systematic and methodical approach that defines computer forensics practice:

    Step 1 — Identification: Finding the potential sources of digital evidence is the first step in the procedure. It could be used to describe a network, computer system, or digital storage device.

    Step 2 — Preservation: The next step after determining the evidence’s sources is to safely preserve them. In order to ensure the integrity of the original data, this method involves creating exact copies, or replicas, of the data.

    Step 3 — Analysis: In order to uncover relevant evidence for the case, the forensic investigator meticulously examines the duplicated data during this phase. The analysis needs to be thorough and often involves using specialist tools to recover corrupted, missing, or encrypted data.

    Step 4 — Documentation: The investigator carefully documents all procedures and results throughout the analysis. Since the documentation may be subjected to a thorough review during legal proceedings, it must be thorough and organized.

    Step 5 — Reporting: Putting together a report that summarizes the research’s findings is the final step. Since this report may be used in a legal context, it should be clear, concise, and understandable to those without technical expertise.

    FAQs

    About Computer Forensic Investigator

    1: What is the salary of a digital forensic analyst?

    A digital forensic analyst’s pay in India depends on a number of variables, including their degree of experience, where they work, and the type of organization hiring them. Generally speaking, entry-level jobs start at about INR 3–4 lakhs annually.  Experienced analysts, however, can earn more than INR 8–12 lakhs annually. Professionals with a great deal of experience or knowledge might be paid more.

    2: How long is a computer forensics degree?

    An undergraduate degree in computer forensics or a closely related field, such as cybersecurity or information technology, normally takes four years to complete full-time. The length of time may vary depending on whether a university offers part-time or expedited programs.

    3: What do computer forensic investigators do?

    To find evidence for court cases or investigations, computer forensic investigators recover and examine data from digital devices, including computers and smartphones. Their duties include locating and preserving digital evidence, analyzing data, creating reports, and often testifying in court about their conclusions.

    4: Can I study forensic science after 12th computer science?

    Yes, after completing your 12th-grade schooling satisfactorily, you can pursue a degree in forensic science with a computer technology concentration. People with a wide range of educational backgrounds can enroll in forensic science degree programs, especially those with a solid foundation in science or technology.

    5: Who needs computer forensics?

    Computer forensics services are required for criminal investigations, civil litigation, internal investigations, cybersecurity, and national security objectives by law enforcement agencies, law firms, corporations, and government institutions. Additionally, they are used by private sector companies to do compliance audits and analyze data breaches, respectively.

    6: Does computer forensics require maths?

    While significant mathematical expertise is not typically required for computer forensics, it is helpful to have a basic understanding of the subject. In forensic investigations, algebra and statistics are useful tools for data analysis and problem-solving.

    7: Does forensic science need NEET?

    Taking the National Eligibility cum Entrance Test (NEET) is not required for forensic science. The primary purpose of the NEET exam is to serve as an admission exam for Indian medical programs. Even though forensic science is a multidisciplinary field that includes biology, chemistry, physics, and law, admission to forensic science colleges typically does not require a NEET qualification.

    Wrapping Up

    In a nutshell, we would like to note that one needs a combination of technical expertise, legal knowledge, and analytical skills to work as a computer forensic investigator. You can pursue a fulfilling career that significantly impacts the fields of cybersecurity and law enforcement by obtaining the necessary training, credentials, and experience. In this ever-evolving field, it is critical to remember that continuous learning and adaptation are necessary.

    summer training program in cyber security at craw Security

    For the same reason, you can enroll in Craw Security’s top-notch, AI-powered one-year diploma program in cybersecurity, which is taught by highly certified and experienced instructors with years of real-world experience in the field.  You can visit the Official Website of Craw Security to find out more about the next batches or any other information, or you can call our hotline mobile number +91-9513805401 to speak with one of our academic counselors.

  • List of Top 30 IoT Interview Questions and Answers [2025]

    List of Top 30 IoT Interview Questions and Answers [2025]

    Top 30 IoT Interview Q&A

    If you want to pass the interview session for a profile related to IoT skills, you can read this amazing article explaining the Top 30 IoT Interview Questions and Answers. Moreover, several industries have already included IoT devices in their working environment to ensure effective and efficient work performance.

    In the end, we are going to talk about a reputed training institute that can offer you a dedicated training program for IoT Skills for students. What are we waiting for? Let’s get straight to the topic!

     

    What is IoT Security?

    A network of physical objects (or “things”) that have sensors, software, and other technologies integrated into them so they can gather and share data with other systems and devices online is known as the Internet of Things (IoT).

    Learn about What is IoT Security

    These items can be anything from commonplace domestic goods to highly advanced industrial equipment. Numerous applications can be remotely monitored, controlled, and automated thanks to the connectivity. Let’s talk about the Top 30 IoT Interview Questions and Answers!

     

    Top 30 IoT Interview Questions and Answers

    1. What is IoT, and how does it work?

    The Internet of Things (IoT) is a network of physical things that have been equipped with technology to gather and share data online. In the following ways, it works:

    1. Things with Sensors/ Actuators,
    2. Connectivity,
    3. Data Processing,
    4. User Interface, and
    5. Action and Automation.

    Related link: What is IoT Security?

    2. What are the challenges of IoT?

    Following are some of the challenges of IoT:

    1. Security,
    2. Interoperability,
    3. Data Management,
    4. Privacy, and
    5. Scalability.

    3. Distinguish Between The IoT and Iiot.

    While IIoT (Industrial IoT) concentrates on industrial applications for efficiency, productivity, and safety, IoT connects commonplace objects for ease and public use.

    4. What are the advantages of IoT?

    Information of What are the advantages of IoT

    The following are the advantages of IoT:

    1. Increased Efficiency,
    2. Enhanced Data Collection and Analysis,
    3. Improved Convenience & Comfort,
    4. Cost Reduction, and
    5. Enhanced Safety & Security.

    5. How Can Edge Computing Benefit IoT?

    In the following ways, Edge Computing can benefit IoT:

    1. Reduced Latency,
    2. Lower Bandwidth Usage,
    3. Enhanced Reliability & Resilience,
    4. Improved Security & Privacy, and
    5. Real-time Insights & Faster Decision-Making.

    6. Explain Bluegiga Apx4 Protocol.

    Often used to create wireless gateways, the Bluegiga APX4 protocol is a low-power wireless System-on-Module that combines Bluetooth Low Energy (BLE) and Wi-Fi on a single platform.

    7. Explain  Pi.

    A Raspberry Pi is a line of tiny, reasonably priced, single-board computers that can be used for several tasks, such as learning to program or creating Internet of Things devices.

    Related link: What Is Raspberry Pi?

    8. List layers of the IoT protocol stack.

    The following are the IoT Protocol stacks:

    1. Perception/ Device Layer,
    2. Network/ Transport Layer,
    3. Processing/ Middleware Layer, and
    4. Application Layer.

    9. Can you explain the IoT architecture/layers (perception, network, application)?

    The perception layer, which is made up of physical devices gathering data, the network layer, which transmits this data, and the application layer, which uses the processed data for certain services, make up the Internet of Things architecture.

    10. What are some popular communication protocols used in IoT?

    Following are some of the popular communication protocols used in IoT:

    1. MQTT (Message Queuing Telemetry Transport),
    2. CoAP (Constrained Application Protocol),
    3. HTTP/ HTTPS (Hypertext Transfer Protocol/Secure),
    4. Bluetooth/ BLE (Bluetooth Low Energy), and
    5. Zigbee.

    11. How does MQTT differ from HTTP in IoT applications?

    Unlike HTTP’s more complex request/response format, MQTT is a lightweight publish/subscribe messaging protocol designed for low-bandwidth and unstable networks, which are widespread in the Internet of Things.

    12. What is the role of sensors and actuators in IoT?

    In the Internet of Things, sensors serve as the “eyes and ears” by gathering information from the physical world, while actuators are the “hands and feet” that carry out physical activities in response to data processing.

    13. What are some common use cases of IoT in 2025?

    The following are some common use cases of IoT in 2025:

    1. Enhanced Smart Homes,
    2. Smarter Healthcare with Remote Monitoring,
    3. Industrial IoT (IIoT) for Predictive Maintenance & Automation,
    4. Smarter & More Sustainable Cities, and
    5. Connected & Autonomous Vehicles.

    14. What is the difference between M2M and IoT?

    In contrast to IoT (Internet of Things), which is a larger network of interconnected devices interacting over the internet for a variety of applications and data sharing, M2M (Machine-to-Machine) usually entails a direct point-to-point connection between devices for specialized activities.

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    15. What encryption techniques are used in IoT communication?

    The following encryption techniques are used in IoT communication:

    1. Transport Layer Security (TLS)/ Datagram Transport Layer Security (DTLS),
    2. Advanced Encryption Standard (AES),
    3. Elliptic Curve Cryptography (ECC),
    4. MQTT Payload Encryption, and
    5. Asymmetric Key Encryption (RSA).

    16. What is the significance of IPv6 in IoT?

    In contrast to IPv4’s restrictions, IPv6’s greatly increased address space makes it significant for the Internet of Things since it can handle the exponentially increasing number of connected devices.

    17. What’s the role of AI and ML in modern IoT systems?

    AI and ML serve as the “brains” of contemporary IoT systems, processing the massive volumes of data gathered by devices to provide automated processes, predictive maintenance, intelligent decision-making, and personalized experiences.

    18. Explain the difference between edge, fog, and cloud computing.

    Cloud computing handles data in centralized, distant data centres Fog computing handles data in a decentralized fashion closer to the edge than the cloud and edge computing handles data at or close to the source devices.

    19. What is a digital twin, and how is it used in IoT?

    A digital twin is a computerized model of a real asset, procedure, or system that is updated in real time using Internet of Things data to mimic, track, and improve its behavior and performance.

    20. What’s the function of a gateway in IoT?

    An IoT gateway serves as a conduit between IoT devices and the cloud, frequently offering edge processing, connectivity, data aggregation, and protocol translation.

    21. How is data collected and analyzed in an IoT system?

    Sensors in devices gather data, which is then sent over networks for processing and analysis—often with cloud-based platforms and AI/ML algorithms—to glean insights and facilitate action in an Internet of Things system.

    22. How do you ensure scalability in a large-scale IoT deployment?

    Designing for modularity, leveraging cloud-based infrastructure, implementing effective data management techniques, and implementing strong network topologies that can manage growing device counts and data volumes are all necessary to ensure scalability in extensive IoT deployments.

    23. What are the best practices for firmware updates in IoT devices?

    The following are the best practices for firmware updates in IoT devices:

    1. Secure Over-the-Air (OTA) Updates,
    2. Thorough Testing and Staged Rollouts,
    3. Robust Rollback Mechanisms,
    4. Clear Communication & User Control (where applicable), and
    5. Minimize Power Consumption During Updates.

    24. How would you handle device identity and authentication in an IoT ecosystem?

    One can handle device identity and authentication in an IoT ecosystem in the following ways:

    1. Unique Device Identity,
    2. Strong Authentication Mechanisms,
    3. Secure Key Management,
    4. Certificate-Based Authentication, and
    5. Role-Based Access Control (RBAC) & Authorization.

    25. How do you monitor and manage thousands of IoT devices?

    One can monitor and manage thousands of IoT devices in the following ways:

    1. Centralized Device Management Platform,
    2. Real-time Monitoring & Alerting,
    3. Remote Diagnostics & Troubleshooting,
    4. Automation & Orchestration, and
    5. Data Analytics & Visualization.

    26. What’s the impact of 5G on IoT?

    By offering faster speeds, less latency, and more capacity, 5G greatly expands IoT capabilities and makes it possible for more sophisticated and real-time applications in a variety of industries.

    27. What cloud services do you use for IoT, and why? (e.g., AWS IoT Core, Azure IoT Hub)

    I don’t utilize any particular cloud services or have any personal preferences because I’m a big language model. Nonetheless, AWS IoT Core and Azure IoT Hub are well-liked options for IoT cloud services because of their extensive device management, data ingestion, analytics, and strong security features made to meet the particular requirements of IoT installations.

    28. How do you ensure data privacy in an IoT system?

    By following the factors below, you can ensure data privacy in an IoT system:

    1. Data Minimization,
    2. Data Encryption,
    3. Anonymization & Pseudonymization,
    4. Secure Device Management & Updates, and
    5. Transparency & User Consent.

    29. What testing strategies do you use for IoT systems?

    The following are some of the testing strategies we can use for IoT systems:

    1. Functional Testing,
    2. Security Testing,
    3. Performance & Scalability Testing,
    4. Interoperability Testing, and
    5. Usability & User Experience (UX) Testing.

    30. Where do you see the future of IoT heading in the next 5 years?

    The future of IoT is anticipated to be characterized in the next five years by expanded use of edge computing for real-time processing, heightened focus on security and privacy, broader adoption of 5G for faster and more dependable connectivity, deeper integration of AI and machine learning for enhanced intelligence and automation, and the expansion of IoT applications into more diverse sectors like healthcare, smart cities, and industrial automation, which will result in more effective, individualized, and connected experiences.

    Benefits of IoT Security in Organizations

    S.No. Benefits How?
    1. Protection of Sensitive Data To avoid unwanted access and possible data breaches, IoT security measures protect sensitive data that is gathered, processed, and transmitted by connected devices.
    2. Prevention of Cyberattacks Strong security measures reduce the possibility of ransomware, malware, and denial-of-service attacks, among other cyber threats, that target IoT networks and devices.
    3. Ensuring Business Continuity By protecting IoT infrastructure, businesses can guarantee uninterrupted operation by avoiding interruptions to vital services and processes that depend on linked devices.
    4. Maintaining Customer Trust and Loyalty Strong IoT security procedures build confidence and enduring loyalty by ensuring that consumers’ data and interactions with linked products are safe.
    5. Compliance with Regulations Organizations can comply with a variety of data protection and privacy rules that are pertinent to IoT installations by putting in place suitable security measures.
    6. Protection of Intellectual Property Protecting linked systems and devices helps stop theft and illegal access to priceless intellectual property created or embedded in IoT solutions.
    7. Enhanced Operational Efficiency By reducing downtime and guaranteeing the integrity of data used for decision-making, secure and dependable IoT solutions help to maintain consistent and optimal operational procedures.
    8. Preserving Brand Reputation An organization’s brand image and reputation are safeguarded by avoiding security incidents and data breaches related to IoT installations.

    This helps to avoid potential harm to consumer perception and market status.

    Top 10 Industries that use IoT Devices in 2025

    Learn about Industries that use IoT Devices

    The following are some of the industries that use IoT devices:

    1. Manufacturing: For supply chain optimization, quality assurance, predictive maintenance, real-time machinery monitoring, and increased automation in smart factories.
    2. Agriculture: Precision farming for climate control in greenhouses, controlled irrigation, tracking animals, soil monitoring, and drone-based crop surveillance.
    3. Healthcare: For medication management, telemedicine, smart hospitals (smart beds, connected medical equipment), and remote patient monitoring (wearables, implanted devices).
    4. Energy: Smart metering for optimal consumption, predictive maintenance of infrastructure, monitoring of renewable energy, and effective energy distribution in smart grids.
    5. Transportation and Logistics: For intelligent traffic management systems, smart warehousing, fleet management, autonomous vehicles, and real-time cargo tracking.
    6. Retail: For automated checkout processes, smart shelves, tailored offers, improved customer experience via linked devices, and inventory management.
    7. Smart Homes and Buildings: For energy management, automated appliances, smart lighting, security systems, and climate control (smart thermostats).
    8. Smart Cities: For public safety, trash management, intelligent traffic control, smart street lighting, and environmental monitoring (air and water quality).
    9. Environmental Monitoring: For weather forecasting, wildlife protection initiatives, pollutant level tracking, and water quality monitoring.
    10. Automotive: Providing infotainment, safety features, navigation, predictive maintenance, and opening the door for autonomous driving in connected automobiles.

     

    Job Profiles After the Internet of Things Penetration Testing Course with AI in Delhi

    S.No. Job Profiles What?
    1. IoT Security Analyst with AI Focus Using AI-powered technologies for threat identification and analysis to analyze security flaws in IoT systems and devices.
    2. AI-Driven IoT Penetration Tester Use AI approaches to conduct sophisticated penetration testing on IoT environments to find intricate and unique attack routes.
    3. IoT Security Architect with AI Expertise Creating safe IoT architectures using threat intelligence and AI-based security features.
    4. AI-Powered Vulnerability Researcher (IoT) Creating and using AI/ML models to find security holes and zero-day vulnerabilities in IoT software and devices.
    5. IoT Threat Intelligence Analyst (AI-Specialized) Utilizing AI to analyze large datasets of IoT security incidents to find new trends, threats, and attacker activities.
    6. Security Automation Engineer (IoT with AI) Creating frameworks for automated security testing and response for Internet of Things systems and incorporating AI to achieve intelligent automation.
    7. IoT Forensic Analyst with AI Capabilities Utilizing AI-driven methods for data analysis and attribution while looking into security incidents and breaches in IoT systems.
    8. AI-Enhanced IoT Security Consultant Offering firms professional counsel and direction on utilizing AI-powered security solutions and protecting their IoT deployments.
    9. Manager/ Lead of IoT Security Testing (with AI Emphasis) Directing groups in charge of IoT system security evaluations and penetration testing, with an emphasis on implementing AI techniques.
    10. Researcher in AI for IoT Security Investigating the use of machine learning and artificial intelligence to improve the security of Internet of Things ecosystems and devices.

    Conclusion

    Now that you have gone through the Top 30 IoT Interview Questions and Answers, you might feel a bit confident about cracking the interview sessions. Apart from that, those who want to make a career in the IoT domain in the IT Industry can get in contact with Craw Security, which offers a dedicated training & certification program, the Internet of Things Penetration Testing Course with AI in Delhi, to students.

    During the training sessions, aspirants will be able to find out various techniques to secure IoT Devices during task completion under the guidance of professionals. With that, online sessions offered by Craw Security will facilitate students’ remote learning.

    After the completion of the Internet of Things Penetration Testing Course with AI in Delhi offered by Craw Security, students will receive a dedicated certificate validating their honed knowledge & skills during the sessions. What are you waiting for? Contact, Now!

  • Top 10 Famous Hackers Who Redefined Cyberspace [2025]

    Top 10 Famous Hackers Who Redefined Cyberspace [2025]

    Top 10 Famous Hackers Who Redefined Cyberspace

    Cyberspace has seen influential individuals who’ve left significant marks on cybersecurity. Whether positive, negative, or somewhere in between, these famous hackers have profoundly reshaped the digital world. Here are the stories of the ten most notable hackers who have influenced cyberspace and cybersecurity practices worldwide.

    1. Kevin Mitnick: From Hacker to Cybersecurity Expert

    Kevin Mitnick was once America’s most notorious hacker, known for infiltrating numerous computer systems. After serving prison time, Mitnick reinvented himself as a cybersecurity consultant, significantly contributing to ethical hacking practices.

    Kevin Mitnick

    2. Julian Assange: Founder of WikiLeaks

    Julian Assange is renowned for creating WikiLeaks, a platform that publishes classified information and news leaks. While some regard Assange as a transparency advocate, others view him as a threat to national security.

    julian assange

    3. Gary McKinnon: Military Systems Hacker

    British hacker Gary McKinnon is famous for allegedly conducting the largest military hack in history, infiltrating 97 U.S. military and NASA computers. McKinnon claimed his goal was to find evidence of UFO cover-ups and free energy technologies.

    Gary McKinnon:

    4. Adrian Lamo: The Homeless Hacker Turned Informant

    Adrian Lamo, known as “The Homeless Hacker,” gained notoriety by penetrating prominent networks like Microsoft, Yahoo!, and The New York Times. He later became a controversial whistleblower by reporting Chelsea Manning, the U.S. Army analyst responsible for extensive data leaks.

    Adrian Lamo

    5. Albert Gonzalez: Mastermind of Major Credit Card Hacks

    Albert Gonzalez orchestrated significant data breaches at TJX, Heartland Payment Systems, and Dave & Buster’s, compromising millions of credit and debit cards. His activities highlighted vulnerabilities in financial cybersecurity.

    Albert Gonzalez

    6. Matthew Bevan and Richard Pryce: Duo Behind U.S. Military Network Hacks

    In the mid-1990s, British hackers Matthew Bevan and Richard Pryce penetrated U.S. military systems, escalating international tensions. Their actions highlighted the geopolitical implications of cybersecurity vulnerabilities.

    7. Jeanson James Ancheta: Botnet Pioneer

    Jeanson James Ancheta created large groups of infected computers called botnets. He was the first person convicted of using these botnets for harmful activities, infecting around 500,000 computers, including U.S. military systems.

    8. Michael Calce (Mafia Boy): The Teen Who Shut Down Major Websites

    At just 15, Michael Calce, known as “Mafia Boy,” launched major cyberattacks that temporarily shut down huge websites like Yahoo!, Amazon, and CNN. His actions highlighted how vulnerable the internet was to cyberattacks.

    9. Kevin Poulsen: From Hacker to Cybersecurity Journalist

    Kevin Poulsen, also known as “Dark Dante,” hacked into telephone systems to win contests, famously winning a Porsche from a radio station. After prison, he became a respected journalist focusing on cybersecurity.

    10. Aaron Swartz: Fighter for Internet Freedom

    Aaron Swartz was a talented programmer and co-founder of Reddit. He believed strongly in free and open internet access. After facing legal trouble for downloading academic articles from JSTOR, he tragically passed away, sparking conversations about online freedom.

    Conclusion

    These individuals have left indelible marks on the digital realm, pushing boundaries and challenging systems. Each person on this list changed how we view and use the digital world. They acted out for different reasons. Some had bad intentions, some were curious, and others wanted to do good.

    Frequently Asked Questions

    Q1: What is a hacker?
    A hacker is someone who uses their technical skills to gain unauthorized access to computer systems, either to exploit vulnerabilities or help improve cybersecurity.

    Q2: Are all hackers criminals?
    No. Some ethical hackers help identify security weaknesses legally, and malicious hackers break into systems for harmful purposes.

    Q3: What is ethical hacking?
    Ethical hacking involves legally and responsibly testing computer systems to find security flaws so they can be fixed.

    Q4: How can I protect myself from hackers?
    You can protect yourself by using strong passwords, updating software regularly, avoiding suspicious emails, and using security tools like antivirus software.

    Q5: Why is cybersecurity important?
    Cybersecurity protects your personal and financial information from being stolen or misused by hackers, ensuring safety online.

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  • How to Avoid Being the Victim of Malicious Ads

    How to Avoid Being the Victim of Malicious Ads

    Avoid Being the Victim of Malvertising

    In today’s digital era, ads aren’t just designed to market products or services; some are specifically crafted with malicious intent. These malicious ads, commonly known as “malvertising,” have seen a worrying rise. Yet, surprisingly, public awareness regarding the threat remains relatively low. Falling victim to such schemes can have devastating effects, from identity theft to substantial financial loss. In this blog, we’ll dive into the world of malvertising and explore five crucial steps you can take to protect yourself.

    Malicious Ads: A Silent Yet Growing Threat

    At first glance, malvertising might seem like a typical ad. However, behind the curtain, it’s embedded with malicious code designed to harm or exploit any system it can infiltrate. What makes malvertising particularly sinister is its ability to slip through even the most reputable ad networks. Therefore, even your favorite, trustworthy websites might unintentionally host these malicious ads.

    5 Ways to Stay Safe from Malvertising:

    1. Always Check URLs Before Clicking

    Hover over any link or ad before clicking it. The URL displayed will give you a hint about where the link leads. If the address seems suspicious, unrelated to the ad’s content, or is a jumbled mess of characters, think twice before clicking.

    2. Confirm the Brand Logo Looks Genuine

    Scammers often try to mimic well-known brands. An out-of-place logo or one that looks slightly distorted is a red flag. Authentic companies invest significantly in their branding, and any deviation from the standard logo should raise eyebrows.

    3. Consider What the Advert Is Asking You to Do

    Always be wary of ads that prompt you to download software, enter personal information, or provide payment details, especially if it’s for a free service or a deal that seems too good to be true.

    4. Be Cautious, No Matter the Credibility of the Website

    Even established, reputable websites can unwittingly host malvertising through their ad networks. Always keep your guard up, irrespective of how credible the site might seem.

    5. Beware of Redirections

    If you click on an ad and it redirects you multiple times or takes you to a site that seems unrelated to the original ad’s content, it’s best to exit immediately. Multiple redirections can be a tactic to disorient the user and make it harder to trace back to the malicious source.

    In Conclusion

    The world of online advertising offers a vast array of opportunities, both for genuine marketers and, unfortunately, cybercriminals. By staying informed and vigilant, you can enjoy your online browsing experience without falling prey to the sinister world of malvertising. Remember, when in doubt, it’s always better to err on the side of caution.

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  • The Growing Risk of Accidental Data Exposure by Generative AI

    The Growing Risk of Accidental Data Exposure by Generative AI

    The Growing Risk of Accidental Data Exposure by Generative AI

    In the contemporary age of AI applications and services, organizations are constantly leveraging the power of machine learning for various tasks. While the benefits are numerous, there’s an underlying risk that’s often overlooked: the potential for accidental data exposure. This concern is particularly prominent with generative AI models, which can unintentionally output sensitive data.

    Source Code: A Vulnerable Asset

    One of the most startling revelations is the frequency with which source code gets exposed. Source code is the backbone of any software, and its exposure can lead to security breaches, intellectual property theft, and other critical issues. This highlights the need for stringent security controls around AI applications, especially those that have access to or are trained on sensitive information.

    Safeguarding Against Data Leaks in Generative AI Applications

    Generative AI can sometimes reproduce snippets of data it has been trained on. Therefore, the need for protective measures is more urgent than ever. Here’s how organizations can adopt a proactive stance:

    1. Regular Reviews and Monitoring
      Organizations need to consistently monitor AI app activity, trends, behaviors, and the sensitivity of the data being processed. This ensures that any anomalies or potential exposures are detected early.
    2. Restrict Access to Non-Essential Apps
      Any application that doesn’t serve a legitimate business purpose or poses a risk to the organization should be blocked. This minimizes unnecessary vulnerabilities.
    3. Leverage Data Loss Prevention (DLP) Policies
      DLP tools can be instrumental in detecting posts or outputs containing sensitive information. This includes but isn’t limited to, source code, regulated data, passwords, keys, and intellectual property.
    4. Implement Real-time User Coaching
      Marrying DLP with real-time user coaching can work wonders. Users can be reminded of company policies related to AI app usage as they interact with the system. This reduces the risk of human error and instills a security-first approach.
    5. Integrate All Security Defenses
      The various security solutions adopted by an organization should not operate in isolation. They must share intelligence and collaborate, ensuring a streamlined and comprehensive security posture.

    Conclusion

    The rise of generative AI has undoubtedly revolutionized many facets of our daily lives and business operations. However, with great power comes great responsibility. Organizations must be aware of the potential risks and take adequate measures to protect sensitive data. With the above steps in place, businesses can strike a balance between harnessing the power of AI and ensuring robust security.

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