- Programming Proficiency: Knowledge of Python, Java, C++,etc for automation and scripting.
- Networking Fundamentals: Understanding of networking protocols, subnetting, firewalls, and routing.
- Cybersecurity Basics: Familiarity with fundamental cybersecurity concepts, including encryption, authentication, access controls, and security protocols.
- Operating Systems Knowledge: Proficiency in using Windows and Linux operating systems.
- Machine Learning Basics: Understanding of machine learning concepts, algorithms, and basic implementation.
- Web Technologies: Understanding of web technologies, including HTTP/HTTPS protocols, and web servers.
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
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
3.1 AI-Based Threat Detection Tools
3.2 Machine Learning Frameworks for Ethical Hacking
3.3 AI-Enhanced Penetration Testing Tools
3.4 Behavioral 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
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
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 Modeling
5.9 Challenges and Ethical Considerations in AI-Driven Penetration Testing
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 Behavioral 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
7.1 Behavioral Biometrics for User Authentication
7.2 Machine Learning Models for User Behavior Analysis
7.3 Network Traffic Behavioral Analysis
7.4 Endpoint Behavioral 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 Behavior Analytics (UEBA)
7.9 Challenges and Considerations in Behavioral Analysis
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 Behavioral Analysis in Incident Response
8.8 Continuous Improvement through Machine Learning Feedback
8.9 Human-AI Collaboration in Incident Handling
9.1 AI-Driven User Authentication Techniques
9.2 Behavioral 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)
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
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
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
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