Certification Overview

Course Introduction Preview

Module 1: Foundation of Ethical Hacking Using Artificial Intelligence (AI)

1.1 Introduction to Ethical Hacking

1.2 Ethical Hacking Methodology

1.3 Legal and Regulatory Framework

1.4 Hacker Types and Motivations

1.5 Information Gathering Techniques

1.6 Footprinting and Reconnaissance

1.7 Scanning Networks

1.8 Enumeration Techniques

Module 2: Introduction to AI in Ethical Hacking

2.1 AI in Ethical Hacking

2.2 Fundamentals of AI

2.3 AI Technologies Overview

2.4 Machine Learning in Cybersecurity

2.5 Natural Language Processing (NLP) for Cybersecurity

2.6 Deep Learning for Threat Detection

2.7 Adversarial Machine Learning in Cybersecurity

2.8 AI-Driven Threat Intelligence Platforms

2.9 Cybersecurity Automation with AI

Module 3: AI Tools and Technologies in Ethical Hacking

3.1 AI-Based Threat Detection Tools

3.2 Machine Learning Frameworks for Ethical Hacking

3.3 AI-Enhanced Penetration Testing Tools

3.4 Behavioural Analysis Tools for Anomaly Detection

3.5 AI-Driven Network Security Solutions

3.6 Automated Vulnerability Scanners

3.7 AI in Web Application

3.8 AI for Malware Detection and Analysis

3.9 Cognitive Security Tools

Module 4: AI-Driven Reconnaissance Techniques

4.1 Introduction to Reconnaissance in Ethical Hacking

4.2 Traditional vs. AI-Driven Reconnaissance

4.3 Automated OS Fingerprinting with AI

4.4 AI-Enhanced Port Scanning Techniques

4.5 Machine Learning for Network Mapping

4.6 AI-Driven Social Engineering Reconnaissance

4.7 Machine Learning in OSINT

4.8 AI-Enhanced DNS Enumeration & AI-Driven Target Profiling

Module 5: AI in Vulnerability Assessment and Penetration Testing

5.1 Automated Vulnerability Scanning with AI

5.2 AI-Enhanced Penetration Testing Tools

5.3 Machine Learning for Exploitation Techniques

5.4 Dynamic Application Security Testing (DAST) with AI

5.5 AI-Driven Fuzz Testing

5.6 Adversarial Machine Learning in Penetration Testing

5.7 Automated Report Generation using AI

5.8 AI-Based Threat Modelling

5.9 Challenges and Ethical Considerations in AI-Driven Penetration Testing

Module 6: Machine Learning for Threat Analysis

6.1 Supervised Learning for Threat Detection

6.2 Unsupervised Learning for Anomaly Detection

6.3 Reinforcement Learning for Adaptive Security Measures

6.4 Natural Language Processing (NLP) for Threat Intelligence

6.5 Behavioural Analysis using Machine Learning

6.6 Ensemble Learning for Improved Threat Prediction

6.7 Feature Engineering in Threat Analysis

6.8 Machine Learning in Endpoint Security

6.9 Explainable AI in Threat Analysis

Module 7: Behavioural Analysis and Anomaly Detection for System Hacking

7.1 Behavioural Biometrics for User Authentication

7.2 Machine Learning Models for User Behaviour Analysis

7.3 Network Traffic Behavioural Analysis

7.4 Endpoint Behavioural Monitoring

7.5 Time Series Analysis for Anomaly Detection

7.6 Heuristic Approaches to Anomaly Detection

7.7 AI-Driven Threat Hunting

7.8 User and Entity Behaviour Analytics (UEBA)

7.9 Challenges and Considerations in Behavioural Analysis

Module 8: AI Enabled Incident Response Systems

8.1 Automated Threat Triage using AI

8.2 Machine Learning for Threat Classification

8.3 Real-time Threat Intelligence Integration

8.4 Predictive Analytics in Incident Response

8.5 AI-Driven Incident Forensics

8.6 Automated Containment and Eradication Strategies

8.7 Behavioural Analysis in Incident Response

8.8 Continuous Improvement through Machine Learning Feedback

8.9 Human-AI Collaboration in Incident Handling

Module 9: AI for Identity and Access Management (IAM)

9.1 AI-Driven User Authentication Techniques

9.2 Behavioural Biometrics for Access Control

9.3 AI-Based Anomaly Detection in IAM

9.4 Dynamic Access Policies with Machine Learning

9.5 AI-Enhanced Privileged Access Management (PAM)

9.6 Continuous Authentication using Machine Learning

9.7 Automated User Provisioning and De-provisioning

9.8 Risk-Based Authentication with AI

9.9 AI in Identity Governance and Administration (IGA)

Module 10: Securing AI Systems

10.1 Adversarial Attacks on AI Models

10.2 Secure Model Training Practices

10.3 Data Privacy in AI Systems

10.4 Secure Deployment of AI Applications

10.5 AI Model Explainability and Interpretability

10.6 Robustness and Resilience in AI

10.7 Secure Transfer and Sharing of AI Models

10.8 Continuous Monitoring and Threat Detection for AI

Module 11: Ethics in AI and Cybersecurity

11.1 Ethical Decision-Making in Cybersecurity

11.2 Bias and Fairness in AI Algorithms

11.3 Transparency and Explainability in AI Systems

11.4 Privacy Concerns in AI-Driven Cybersecurity

11.5 Accountability and Responsibility in AI Security

11.6 Ethics of Threat Intelligence Sharing

11.7 Human Rights and AI in Cybersecurity

11.8 Regulatory Compliance and Ethical Standards

11.9 Ethical Hacking and Responsible Disclosure

Module 12: Capstone Project

12.1 Case Study 1: AI-Enhanced Threat Detection and Response

12.2 Case Study 2: Ethical Hacking with AI Integration

12.3 Case Study 3: AI in Identity and Access Management (IAM)

12.4 Case Study 4: Secure Deployment of AI Systems

Optional Module: AI Agents for Ethical Hacking

1. Understanding AI Agents

2. Case Studies

3. Hands-On Practice with AI Agents