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Program Highlights

The AI-Powered Cybersecurity Training course provided by InfosecTrain is a cutting-edge program designed to enhance your expertise in AI-driven threat detection and prevention. This comprehensive course covers advanced cybersecurity techniques, AI applications, and practical strategies to protect digital assets. Perfect for professionals aiming to stay ahead in the evolving cybersecurity landscape, the course empowers you to master AI-based solutions.

  • 40-Hour of Instructor-led Training40-Hour of Instructor-led Training
  • Career-oriented Skill-based CourseCareer-oriented Skill-based Course
  • Learn with Real-World ScenariosLearn with Real-World Scenarios
  • Highly Interactive and Dynamic SessionsHighly Interactive and Dynamic Sessions
  • Immersive LearningImmersive Learning
  • Learn from Industry ExpertsLearn from Industry Experts
  • Career Guidance and MentorshipCareer Guidance and Mentorship
  • Extended Post Training SupportExtended Post Training Support
  • Access to Recorded SessionsAccess to Recorded Sessions

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About Course

InfosecTrain’s AI-Powered Cybersecurity Training Course is a comprehensive program tailored to meet the demands of today’s rapidly evolving digital landscape. The course delves into integrating Artificial Intelligence with cybersecurity, providing participants with advanced skills to detect, analyze, and counter cyber threats efficiently. The course covers the fundamental concepts of Python programming, which is crucial for participants to learn and apply due to its relevance and versatility in AI and cybersecurity.

Through hands-on exercises, case studies, and industry-relevant scenarios, learners gain practical experience to tackle real-world challenges. Designed for IT Professionals, Cybersecurity Specialists, Data Engineers, and enthusiasts, the course provides learners a competitive edge to master advanced AI technologies to safeguard digital ecosystems effectively and sustainably.

Course Curriculum
  • Module 1. Foundations of Python Programming
    • Python Fundamentals and Core Concepts
    • Introduction to Basic Python Commands
    • Python Variables, Operators, Datatypes (Lists, Tuples, Dictionaries), Modules, Functions, Control Flow, Randomness, Regular Expressions
    • Python Libraries for AI
    • Practical: Using Numpy, Pandas, Matplotlib, Scikit-learn, Tensorflow, Keras, PyTorch
    • Practical: Hands-on with Jupyter Notebook, Google Colab, ChatGPT, Claude, etc.
  • Module 2. Introduction to AI
    • What is AI?
    • History and Development of AI
    • AI – Current Scenario
    • AI Applications
      • Descriptive, Predictive, Prescriptive, and Generative applications
      • Classification and Regression
      • Automation
      • Reactive vs Predictive Analysis
      • Anomaly Detection
      • Behavior Analysis
    • AI Types and Categories
      • Machine Learning and its types: Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning
      • Deep Learning: Perceptrons, MLP, ANN, CNN, RNN, LSTM, GAN
      • Natural Language Processing and LLMs
    • Challenges with AI
      • Context and Alignment
      • Explainable AI
      • Hallucinations and Grounding
      • AI Bias
      • Regulation and Compliance
      • AI Ethics, Data Privacy, Human Rights, Intellectual Property Issues
      • NIST AI Risk Management Framework
    • Data Science and Feature Engineering
      • Data Pre-processing: Data Collection, Cleaning, Integration, and Transformation
      • Feature Engineering: Creation, Selection, and Extraction
      • Dimensionality Reduction
      • Feature Scaling, Normalization, and Standardization
      • Encoding Techniques
      • Handling Imbalanced Data
      • Data Quality Assessment
  • Module 3. Introduction to Cybersecurity
    • Basic Security Concepts and Cybersecurity Roles
    • Threat Types and Landscape
    • Traditional Cybersecurity vs AI-powered Cybersecurity
    • Using AI for Penetration Testing
    • AI in Cybersecurity Applications
      • Access Controls
      • Identity and Access Management (IAM)
      • Threat Detection and Prevention Techniques
      • Vulnerability Assessment
      • Threat Intelligence, Hunting, and Analysis
      • Monitoring with SIEM and SOAR
      • Endpoint Protection – EDR and XDR
      • Incident Response
      • Digital Forensics
    • Case Studies
    • Practical: Machine Learning Lifecycle
    • Domain-Specific Preprocessing
      • Security Log, Network Packet, Spam/Phishing, Malware Binary, User Behavior, and Authentication Data Preprocessing
  • Module 4. Adversarial Attacks on AI
    • Types of Attacks
      • Evasion, Poisoning, Model Extraction
    • OWASP Machine Learning Security Top Ten
    • Mitigation Techniques
  • Module 5. Supervised Machine Learning for Cybersecurity
    • Classification and Regression Problems and Understanding ML Algorithms: Linear Regression, Logistic Regression, SVMs, Decision Trees, Naive Bayes
    • Practical: Implementing a Network Scanner (Scapy Library)
    • Network Traffic Monitoring and Log Collection
    • Converting Network Logs into Datasets
    • Practical: Spam/Phishing Detection
    • Understanding Classification Reports and Confusion Matrix
    • Optimization Strategies and Ensemble Learning: Bagging, Boosting, Stacking
  • Module 6. Unsupervised Machine Learning for Cybersecurity
    • Unsupervised ML Algorithms
    • Model creation using Clustering Algorithms
    • Practical: Network Anomaly Detection
    • Types of Network Attacks and Best ML Algorithms for different scenarios
    • Practical: Botnet Detection
    • Practical: Intrusion Detection System
  • Module 7. Neural Networks and Deep Learning for Cybersecurity
    • Neural Network Basics
      • Perceptrons, Activation Functions, Gradient Descent, Backpropagation
      • Multi-Layer Perceptrons
      • Practical: Building a Spam Detector using perceptrons
    • Deep Learning Algorithms
      • Feedforward Neural Networks (FFNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs)
      • Python Libraries for Deep Learning
      • Practical: Handwritten digit recognition
  • Module 8. Protecting Endpoints Using AI
    • Malware Detection
    • Malware Types and Detection Tools
    • Practical: Signature Detection with Hash Values
    • Rule-based Malware Detection using YARA
    • Practical: Heuristics-based Detection with PE File Headers
    • Dynamic Behavior Analysis with Cuckoo Sandbox
    • Practical: Malicious URL Detection
    • Decision Trees and Random Forest Algorithms, Gradient Boost, and AdaBoost Techniques
    • Polymorphic Malware Detection using HMMs
    • Malware Detection with Deep Learning
    • Practical: Malware Detection from Images using CNNs
  • Module 9. Natural Language Processing (NLP)
    • Text Processing Basics: Tokenization, Stemming/Lemmatization, Stop Words, N-grams
    • Traditional NLP: Bag of Words, TF-IDF, Word2Vec, GloVe
    • Practical: Spam Detection using NLP (NLTK Library, TF-IDF)
    • Practical: System Log Threat Detection
  • Module 10. Identity, Access Management, and Data Protection using AI
    • User Identification and Authentication
    • UEBA, Authentication Abuse Prevention
    • Practical: Password Strength Determination
    • Practical: Keystroke Recognition Authentication
    • Usability vs Security
    • Practical: Biometric Authentication using Facial Recognition
    • Dimensionality Reduction, Eigenvalues, Eigenvectors, Eigenfaces
    • Practical: ML-Based Steganography for Data Protection
    • HIPAA Data Breaches: Exploration and Visualization
  • Module 11. Threat Hunting, Incident Response, and Forensics using AI
    • Methodologies and Models: Cyber Kill Chain, Diamond Model of Intrusion Analysis
    • Predictive Analytics in Incident Response
    • Digital Forensics
    • Practical: AI-assisted Threat Hunting and Forensics using ELK Stack
  • Module 12. Reinforcement Learning and Generative Adversarial Networks (GANs)
    • Introduction to GANs
    • Generators, Discriminators, Loss Functions, Training Process
    • Synthetic Data Generation
    • Practical: Pen Testing Networks with GANs
    • Bypassing Malware Detectors with MalGANs
    • Bypassing Machine Learning Systems with Reinforcement Learning
  • Module 13. Introduction to Generative AI and LLMs
    • History and Development: Transformers Architecture, Attention, BERT, GPT Models
    • Prompt Engineering concepts
    • Using Generative AI in Cybersecurity
      • Governance, Risk, and Compliance
      • Security Awareness, Code Analysis, and Secure Development
      • Vulnerability Assessment, Red Teaming, and Penetration Testing
      • Threat Monitoring and Detection
      • Incident Response
    • OWASP Top 10 for LLM Applications
  • Module 14. Implementing AI Security Controls
    • Data Lifecycle Security: Encryption, Anonymization, Access Controls
    • Secure AI Model Development and Deployment
    • AI Robustness and Validation
    • AI System Monitoring and Auditing
    • Practical:MITRE ATLAS

Target Audience
  • Beginners in IT field
  • Any IT professional who wants to power their transition to cybersecurity with AI
  • Beginners in Cybersecurity
  • Cybersecurity professionals who want to know the basics of using AI to enhance cybersecurity 
  • Data Scientists, Data Engineers, and AI Engineers who want to transition to cybersecurity
Pre-requisites

Programming fundamentals and basic cybersecurity concepts would be beneficial, though we will revisit these basics in this course.

Course Objectives

You will be able to:

  • Develop a strong foundation in Python programming for cybersecurity applications.
  • Understand AI fundamentals and their role in enhancing cybersecurity.
  • Apply supervised and unsupervised machine learning for threat detection.
  • Explore neural networks and deep learning for advanced cybersecurity solutions.
  • Analyze and defend against adversarial attacks on AI models.
  • Implement AI-driven endpoint protection for proactive threat mitigation.
  • Leverage NLP for phishing detection, log analysis, and threat intelligence.
  • Strengthen identity, access management, and data protection using AI.
  • Use reinforcement learning and GANs for attack simulations and defense strategies.
  • Explore generative AI and LLMs for innovative cybersecurity applications.
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Frequently Asked Questions

What is the AI-Powered Cybersecurity Training course?

The AI-Powered Cybersecurity Training is a comprehensive program that integrates artificial intelligence with cybersecurity practices. It equips participants with the skills to leverage AI tools while covering topics such as Python programming, machine learning, adversarial attacks, and endpoint protection, preparing learners to tackle modern cybersecurity challenges effectively.

What career opportunities can this course prepare me for?

  • Security Analyst with an AI focus
  • AI Security Engineer
  • Junior Threat Analyst
  • Security Operations Center (SOC) Analyst
  • AI-assisted Penetration Tester
  • Data Scientist

How can this course support my career transition?

This course bridges the gap between AI and cybersecurity by:

  • Establishing a strong foundation in both domains
  • Offering hands-on experience with industry-relevant tools
  • Teaching current best practices and techniques

This course equips you with essential skills to excel in the growing field of AI-driven cybersecurity.

I have no prior experience with AI or Machine Learning. Will I be able to keep up?

Yes! This course is designed for beginners, starting with fundamental AI concepts and gradually progressing to more advanced topics. The initial modules focus on building a solid foundation, ensuring you can confidently keep up and succeed.

Can I take this course if I'm new to both AI and cybersecurity?

Yes! This course is beginner-friendly and designed to help you get started in both fields. It:

  • Covers the fundamentals of AI and cybersecurity, along with introductory Python programming.
  • Provides supplementary resources for better understanding.
  • Uses practical examples to bridge the gap between the two domains.

Is programming knowledge required?

No, programming knowledge is not mandatory. The course starts with the basics and builds your skills as you progress. Having prior experience may help you understand programming exercises more quickly, but it is not a prerequisite.

Does the course include hands-on training?

Yes! The course perfectly balances theory and hands-on practice with a 50:50 approach. Each module starts with a theoretical foundation, followed by practical applications. Beginning with basic Python exercises, it gradually progresses to real-world problem-solving. Modules include:

  • Interactive demonstrations
  • Guided lab exercises
  • Real-world security scenarios
  • Practical tool implementation

Does this course include LLM pentesting?

The course provides a theoretical overview of threats to LLMs, explores the OWASP Top 10 for LLMs, and introduces the basics of performing pentesting on LLMs.

How can I enroll in an AI-Powered Cybersecurity course?

Enroll in the AI-Powered Cybersecurity Training at InfosecTrain:

  • Visit the InfosecTrain website, www.infosectrain.com, and navigate the AI-Powered Cybersecurity training page.
  • Fill out the registration form.
  • You will get a confirmation email with further instructions.
  • Book your free demo with Expert.
  • You can also directly drop mail with your requirements at sales@infosectrain.com.

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