SEC535: Offensive AI - Attack Tools and Techniques Expert - Led Video Course
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Lesson 1: Introduction to Offensive AI
1.1 Definition of Offensive AI
1.2 Historical Context
1.3 Key Terminologies
1.4 Offensive vs. Defensive AI
1.5 Use Cases in Cybersecurity
1.6 AI in Penetration Testing
1.7 Role of Machine Learning
1.8 Attack Surface Expansion
1.9 Legal and Ethical Considerations
1.10 Overview of the Course
Lesson 2: Fundamentals of Machine Learning for Attackers
2.1 Supervised Learning Overview
2.2 Unsupervised Learning in Attacks
2.3 Reinforcement Learning for Adversaries
2.4 Feature Engineering for Attacks
2.5 Data Preprocessing Techniques
2.6 Model Evaluation Metrics
2.7 Hyperparameter Tuning in Offensive Models
2.8 Transfer Learning in Cyber Attacks
2.9 Model Selection Strategies
2.10 Bias and Fairness Concerns in Offensive AI
Lesson 3: AI-Driven Reconnaissance
3.1 Automated Scanning Techniques
3.2 Natural Language Processing for Information Gathering
3.3 AI-powered OSINT Tools
3.4 Entity Recognition in Reconnaissance
3.5 Anomaly Detection in Network Mapping
3.6 Image Recognition for Target Identification
3.7 Social Media Mining with AI
3.8 Graph Analysis of Relationships
3.9 Real-time Reconnaissance Automation
3.10 Counter-Reconnaissance Evasion
Lesson 4: AI-Powered Vulnerability Discovery
4.1 Automated Fuzzing with AI
4.2 Deep Learning for Exploit Detection
4.3 Static vs. Dynamic Analysis
4.4 Source Code Mining
4.5 Binary Analysis with Neural Networks
4.6 AI-based Patch Generation
4.7 Prioritizing Vulnerabilities Using ML
4.8 NLP for Vulnerability Reports
4.9 AI in Zero-day Discovery
4.10 Integration with Traditional Scanners
Lesson 5: Adversarial Machine Learning
5.1 Introduction to Adversarial Examples
5.2 White-box vs. Black-box Attacks
5.3 Evasion Attacks
5.4 Poisoning Attacks
5.5 Model Inversion
5.6 Membership Inference
5.7 Generative Adversarial Networks (GANs)
5.8 Transferability of Attacks
5.9 Defenses and Countermeasures
5.10 Case Studies in Adversarial ML
Lesson 6: AI-Powered Phishing
6.1 Email Content Generation with AI
6.2 Voice Phishing (Vishing) Automation
6.3 Image-based Phishing with GANs
6.4 Social Engineering Chatbots
6.5 Customization via Data Mining
6.6 Bypass Detection with AI
6.7 Phishing Website Generation
6.8 Real-time Phishing Campaigns
6.9 AI in Spear Phishing
6.10 Phishing Detection Evasion
Lesson 7: Offensive AI in Malware Development
7.1 AI-generated Malware Code
7.2 Polymorphic Malware with ML
7.3 AI-based Evasion Techniques
7.4 Automated Payload Generation
7.5 Steganography using AI
7.6 Malware Command and Control (C2) with AI
7.7 AI-driven Keylogging
7.8 Fileless Malware with ML
7.9 Self-mutating Malware
7.10 Case Studies in AI-powered Malware
Lesson 8: AI for Password Attacks
8.1 Password Cracking with Deep Learning
8.2 Predictive Text-based Attacks
8.3 AI in Brute Force Optimization
8.4 Smart Dictionary Attack Generation
8.5 Password Similarity Analysis
8.6 Neural Networks for Hash Cracking
8.7 Bypassing CAPTCHA with AI
8.8 Phonetic and Semantic Guessing
8.9 AI in Credential Stuffing
8.10 Password Attack Automation
Lesson 9: Offensive AI in Network Attacks
9.1 AI-driven Network Scanning
9.2 Automated Lateral Movement
9.3 AI for Traffic Manipulation
9.4 Anomaly-based Intrusion Evasion
9.5 AI-powered Packet Crafting
9.6 Automated VLAN Hopping
9.7 AI in Man-in-the-Middle Attacks
9.8 Dynamic Protocol Analysis
9.9 AI for Network Segmentation Bypass
9.10 Case Studies in Network Attacks
Lesson 10: AI for Web Application Attacks
10.1 Automated SQL Injection with AI
10.2 XSS Attack Generation using ML
10.3 AI to Bypass Web Application Firewalls
10.4 Form and Input Mining
10.5 Logic Flaws Discovery
10.6 Automated Session Hijacking
10.7 AI in CSRF Attack Automation
10.8 AI-driven Directory Traversal
10.9 Webshell Generation with AI
10.10 Evasion of Web Application Defenses
Lesson 11: AI in Social Engineering
11.1 Automated Social Profile Generation
11.2 Deepfake Video for Impersonation
11.3 AI-powered Voice Cloning
11.4 Social Graph Analysis
11.5 Targeted Messaging with NLP
11.6 AI in Pretexting
11.7 Psychological Profiling Automation
11.8 Real-time Interaction Bots
11.9 AI for Elicitation Techniques
11.10 Social Engineering Attack Simulation
Lesson 12: Data Poisoning Attacks
12.1 Fundamentals of Data Poisoning
12.2 Poisoning Supervised Datasets
12.3 Label Flipping Techniques
12.4 Backdoor Attacks
12.5 Clean-label Attacks
12.6 Trigger Generation Methods
12.7 Detecting Poisoned Data
12.8 Impact Assessment
12.9 Real-world Poisoning Scenarios
12.10 Countermeasures for Data Poisoning
Lesson 13: Offensive AI for Cloud Security
13.1 Cloud Reconnaissance Automation
13.2 AI for Cloud Misconfiguration Discovery
13.3 Automated Credential Harvesting
13.4 AI-based API Attacks
13.5 Cloud Storage Enumeration
13.6 Lateral Movement in Cloud Environments
13.7 AI-driven Data Exfiltration
13.8 Cloud-native Malware
13.9 AI in Cloud Denial of Service
13.10 Cloud Security Bypass Techniques
Lesson 14: AI for IoT Attacks
14.1 Automated IoT Device Discovery
14.2 AI in Firmware Analysis
14.3 Exploiting IoT Protocols
14.4 Automated Default Credential Attacks
14.5 AI-driven Botnet Creation
14.6 Real-time IoT Surveillance
14.7 AI for IoT Network Segmentation Bypass
14.8 Device Spoofing with ML
14.9 IoT Data Exfiltration
14.10 IoT Attack Case Studies
Lesson 15: AI in Evasion Techniques
15.1 Evasion Overview
15.2 Polymorphic Attack Automation
15.3 Signature-based Detection Bypass
15.4 AI-driven Sandbox Evasion
15.5 AI in Anti-Forensics
15.6 AI for Stealth Communication
15.7 AI-based Traffic Shaping
15.8 Automated Timing Attacks
15.9 Adaptive Evasion Strategies
15.10 Counter-Evasion Technologies
Lesson 16: Automated Exploit Generation
16.1 Introduction to Automated Exploits
16.2 Symbolic Execution with AI
16.3 Vulnerability Chaining
16.4 Deep Reinforcement Learning in Exploits
16.5 AI for Exploit Reliability
16.6 Automated Exploit Customization
16.7 Real-time Exploit Generation
16.8 Exploit Delivery Automation
16.9 AI in Post-exploit Actions
16.10 Case Studies in Automated Exploits
Lesson 17: AI for Wireless Attacks
17.1 Automated Wireless Reconnaissance
17.2 AI in WPA/WPA2 Key Cracking
17.3 Bluetooth Attack Automation
17.4 Automated Evil Twin Attacks
17.5 AI for Frequency Hopping
17.6 Signal Jamming with AI
17.7 AI in Rogue Access Point Detection Bypass
17.8 Wireless Protocol Fuzzing
17.9 Automated Session Hijacking
17.10 Real-world Wireless Attack Scenarios
Lesson 18: AI in Physical Security Bypass
18.1 Image Recognition for Physical Access
18.2 AI in RFID Attack Automation
18.3 Automated Badge Cloning
18.4 Facial Recognition Spoofing
18.5 Voiceprint Bypass with AI
18.6 Smart Lock Bypass Automation
18.7 AI in Surveillance Evasion
18.8 Physical Intrusion Planning
18.9 AI-driven Alarm System Evasion
18.10 Case Studies in Physical Security Attacks
Lesson 19: AI for Supply Chain Attacks
19.1 Supply Chain Attack Overview
19.2 Automated Vendor Reconnaissance
19.3 AI for Package Tampering Detection Evasion
19.4 AI in Software Dependency Analysis
19.5 Automated Distribution Path Mapping
19.6 AI in Firmware Supply Chain Attacks
19.7 Supply Chain Attack Simulation
19.8 AI-driven Counterfeit Detection Evasion
19.9 Real-time Supply Chain Monitoring Bypass
19.10 Notable Supply Chain Attacks
Lesson 20: AI for Ransomware Campaigns
20.1 Ransomware Overview
20.2 AI-generated Payloads
20.3 Automated Target Profiling
20.4 Smart Ransom Note Generation
20.5 AI for Lateral Movement in Ransomware
20.6 AI-driven Data Encryption
20.7 Bypassing Backups with AI
20.8 Real-time Negotiation Bots
20.9 Automated Payment Tracking
20.10 Case Studies in AI-powered Ransomware
Lesson 21: AI for Command and Control (C2)
21.1 C2 Fundamentals
21.2 AI-driven C2 Channel Selection
21.3 Adaptive Communication Protocols
21.4 Automated C2 Evasion
21.5 Steganographic C2 with AI
21.6 Real-time Command Generation
21.7 Machine Learning for Beaconing
21.8 AI in C2 Infrastructure Discovery
21.9 C2 Detection Evasion
21.10 Case Studies in AI-powered C2
Lesson 22: Automated Attack Campaign Orchestration
22.1 Campaign Planning with AI
22.2 Target Selection Automation
22.3 AI for Attack Chain Automation
22.4 Resource Allocation Optimization
22.5 Automated Phishing and Malware Delivery
22.6 AI in Multi-stage Attack Coordination
22.7 Adaptive Attack Path Selection
22.8 Real-time Campaign Adjustment
22.9 AI for Attack Metrics and Feedback
22.10 Case Studies in Orchestrated Campaigns
Lesson 23: AI for Insider Threats
23.1 Insider Threat Definition
23.2 Automated Behavioral Analysis
23.3 Social Graph Mining
23.4 AI for Data Exfiltration Detection Evasion
23.5 User Profiling with ML
23.6 Automated Privilege Escalation
23.7 AI for Anomaly Injection
23.8 Insider Attack Simulation
23.9 AI in Policy Bypass
23.10 Case Studies in Insider Threats
Lesson 24: AI in Data Exfiltration
24.1 Data Exfiltration Overview
24.2 Automated Data Collection
24.3 AI for Stealth Exfiltration
24.4 C2 Channel Selection with AI
24.5 Data Obfuscation using ML
24.6 AI in Data Compression for Exfiltration
24.7 Real-time Detection Evasion
24.8 Network Segmentation Bypass
24.9 AI for Exfiltration Path Optimization
24.10 Case Studies in Data Exfiltration
Lesson 25: AI in Privacy Attacks
25.1 Privacy Attack Fundamentals
25.2 Automated Sensitive Data Discovery
25.3 AI for Re-identification
25.4 Inference Attacks using ML
25.5 Automated Metadata Analysis
25.6 Social Media Privacy Evasion
25.7 AI in Facial Recognition Attacks
25.8 Location Privacy Attacks
25.9 AI-driven De-anonymization
25.10 Case Studies in Privacy Attacks
Lesson 26: AI for Bypassing Authentication
26.1 Authentication Attack Overview
26.2 Automated Credential Harvesting
26.3 AI-driven Biometric Spoofing
26.4 Social Engineering Bots for Authentication
26.5 AI in Multi-factor Authentication Bypass
26.6 Automated Session Hijacking
26.7 Machine Learning for Pattern Recognition
26.8 AI in Token Forgery
26.9 Real-time Authentication Evasion
26.10 Case Studies in Authentication Attacks
Lesson 27: AI for Denial of Service (DoS)
27.1 DoS and DDoS Fundamentals
27.2 Traffic Generation Automation
27.3 AI for Botnet Coordination
27.4 Dynamic Attack Pathways
27.5 Automated Targeting and Scaling
27.6 AI in Application-layer Attacks
27.7 Evasion of DoS Defenses
27.8 Adaptive Attack Techniques
27.9 AI in Resource Depletion
27.10 Case Studies in DoS Attacks
Lesson 28: AI in Evasive Traffic Generation
28.1 Traffic Evasion Overview
28.2 AI for Protocol Mimicry
28.3 Encrypted Traffic Generation
28.4 AI-driven Payload Fragmentation
28.5 Timing Obfuscation
28.6 Traffic Shaping with ML
28.7 Automated Tunneling
28.8 Dynamic Path Selection
28.9 Detection Avoidance Strategies
28.10 Real-world Examples
Lesson 29: AI for Cross-platform Attacks
29.1 Cross-platform Attack Fundamentals
29.2 Automated Environment Detection
29.3 AI for Payload Adaptation
29.4 Multi-OS Exploit Generation
29.5 Real-time Platform Switching
29.6 AI in Container Attacks
29.7 Cloud-to-Endpoint Attack Automation
29.8 Mobile and IoT Platform Attacks
29.9 Case Studies in Cross-platform Attacks
29.10 Countermeasures
Lesson 30: AI for Evasion of Security Analytics
30.1 Security Analytics Evasion Overview
30.2 Model Evasion Techniques
30.3 AI in Log Manipulation
30.4 Automated Alert Suppression
30.5 AI-driven False Positive Generation
30.6 Real-time Analytics Bypass
30.7 Obfuscation of Attack Traces
30.8 AI for SIEM Evasion
30.9 Bypassing UEBA Systems
30.10 Analytics Evasion Case Studies
Lesson 31: AI in Advanced Persistent Threats (APTs)
31.1 APT Fundamentals
31.2 AI-driven Target Profiling
31.3 Automated Initial Compromise
31.4 AI in Lateral Movement
31.5 Persistent Access Automation
31.6 AI for Stealth Operations
31.7 Real-time APT Orchestration
31.8 Adaptive Attack Strategies
31.9 Detection Evasion Techniques
31.10 APT Case Studies
Lesson 32: AI for Red Team Automation
32.1 Red Teaming with AI
32.2 Automated Attack Simulation
32.3 AI-driven Penetration Testing
32.4 Scenario Generation Automation
32.5 AI in Social Engineering Simulations
32.6 Payload Generation and Delivery
32.7 Automated Reporting with ML
32.8 Adaptive Blue Team Evasion
32.9 AI for Red Team Collaboration
32.10 Case Studies in Red Team Automation
Lesson 33: AI for Exploit Kits
33.1 Exploit Kit Overview
33.2 AI in Exploit Kit Development
33.3 Automated Vulnerability Matching
33.4 Payload Selection Automation
33.5 AI for Delivery Mechanism Optimization
33.6 Adaptive Exploit Chains
33.7 Real-time Kit Customization
33.8 AI in Exploit Obfuscation
33.9 Tracking Kit Effectiveness
33.10 Case Studies in Exploit Kits
Lesson 34: Offensive AI Frameworks and Tools
34.1 Overview of Offensive AI Frameworks
34.2 Open Source Tools
34.3 Commercial Offerings
34.4 Integration with Existing Toolkits
34.5 Automation Workflows
34.6 Custom AI Model Integration
34.7 Tool Evaluation Criteria
34.8 Framework Deployment
34.9 Case Studies
34.10 Future Directions
Lesson 35: AI in Security Testing Automation
35.1 Security Testing Overview
35.2 Automated Vulnerability Scanning
35.3 Fuzz Testing with AI
35.4 Penetration Testing Automation
35.5 AI-driven Test Case Generation
35.6 Real-time Test Reporting
35.7 Model-based Testing
35.8 AI for Regression Testing
35.9 Security Test Orchestration
35.10 Case Studies
Lesson 36: AI for Bypassing Threat Intelligence
36.1 Threat Intelligence Fundamentals
36.2 AI in Indicator Evasion
36.3 Automated Threat Attribution Bypass
36.4 Evasion of Blacklists and Blocklists
36.5 AI for Threat Actor Profile Manipulation
36.6 Threat Feed Obfuscation
36.7 AI in Threat Correlation Evasion
36.8 Automated Threat Simulation
36.9 Case Studies
36.10 Countermeasures
Lesson 37: AI in Mobile Attacks
37.1 Mobile Attack Overview
37.2 Automated App Analysis
37.3 AI in Mobile Malware Generation
37.4 Bypassing Mobile Security Controls
37.5 Automated Phishing on Mobile
37.6 AI for Mobile Data Exfiltration
37.7 Mobile OS Exploitation
37.8 AI in App Store Evasion
37.9 Real-time Monitoring and Attack
37.10 Case Studies
Lesson 38: AI for Evasion of Endpoint Detection and Response (EDR)
38.1 EDR Overview
38.2 AI in Endpoint Attack Simulation
38.3 EDR Bypass Techniques
38.4 AI-driven Process Injection
38.5 Real-time Detection Evasion
38.6 Automated Persistence Mechanisms
38.7 EDR Log Manipulation
38.8 Adaptive EDR Evasion
38.9 AI for Endpoint Reconnaissance
38.10 Case Studies
Lesson 39: AI for Fileless Attacks
39.1 Fileless Attack Fundamentals
39.2 Memory-based Attack Automation
39.3 AI for Script-based Attacks
39.4 Living-off-the-land (LOL) Attacks
39.5 AI in Process Hollowing
39.6 Automated In-memory Payloads
39.7 AI for Command Execution
39.8 Real-time Fileless Attack Coordination
39.9 Detection Evasion Techniques
39.10 Case Studies
Lesson 40: AI in Evasion of Network Detection and Response (NDR)
40.1 NDR Fundamentals
40.2 AI-driven Traffic Shaping
40.3 NDR Bypass Techniques
40.4 Automated Packet Manipulation
40.5 AI in Stealth Network Communication
40.6 Real-time Detection Evasion
40.7 Adaptive Network Attack Strategies
40.8 Automated NDR Alert Suppression
40.9 Case Studies
40.10 Countermeasures
Lesson 41: AI for Bypassing Deception Technologies
41.1 Deception Technology Overview
41.2 AI in Honeypot Detection
41.3 Automated Deception Evasion
41.4 AI-driven Decoy Identification
41.5 Real-time Deception Mapping
41.6 Adaptive Attack Path Selection
41.7 AI for Decoy Resource Avoidance
41.8 Deception Analytics Evasion
41.9 Case Studies
41.10 Countermeasures
Lesson 42: AI in Threat Emulation
42.1 Threat Emulation Overview
42.2 AI for Attack Scenario Generation
42.3 Automated Payload Creation
42.4 Adaptive Threat Emulation
42.5 AI-driven Red Teaming
42.6 Threat Emulation Metrics
42.7 Real-time Feedback Integration
42.8 AI in Reporting Automation
42.9 Case Studies
42.10 Future Trends
Lesson 43: AI in Attack Attribution Evasion
43.1 Attribution Evasion Fundamentals
43.2 AI-driven TTP (Tactics, Techniques, Procedures) Manipulation
43.3 Automated Language and Style Obfuscation
43.4 AI for Infrastructure Hopping
43.5 Real-time Attribution Avoidance
43.6 AI in False Flag Operations
43.7 Automated Evidence Removal
43.8 Adaptive Attribution Evasion
43.9 Case Studies
43.10 Countermeasures
Lesson 44: AI for Automated Exploit Marketplaces
44.1 Exploit Marketplace Overview
44.2 AI-driven Exploit Valuation
44.3 Automated Exploit Matching
44.4 AI in Buyer/Seller Anonymity
44.5 Real-time Marketplace Monitoring
44.6 Automated Exploit Delivery
44.7 AI-driven Trust Evaluation
44.8 Marketplace Fraud Detection Evasion
44.9 Case Studies
44.10 Future Trends
Lesson 45: AI for Large-scale Automated Campaigns
45.1 Large-scale Attack Fundamentals
45.2 AI in Botnet Management
45.3 Automated Target List Generation
45.4 Campaign Scaling with AI
45.5 Real-time Attack Coordination
45.6 Adaptive Campaign Adjustments
45.7 AI for Multi-vector Attacks
45.8 Metrics and Reporting Automation
45.9 Case Studies
45.10 Future Outlook
Lesson 46: AI in Bypassing Application Security Controls
46.1 Application Security Overview
46.2 AI for Input Validation Bypass
46.3 Automated Privilege Escalation
46.4 AI in Session Management Attacks
46.5 Automated Logic Flaw Exploitation
46.6 AI for Security Policy Evasion
46.7 Adaptive Application Attack Techniques
46.8 AI-driven Application Reconnaissance
46.9 Detection Evasion Strategies
46.10 Case Studies
Lesson 47: Future Trends in Offensive AI
47.1 Evolving Offensive AI Technologies
47.2 Next-generation Attack Techniques
47.3 AI Arms Race in Cybersecurity
47.4 Quantum Computing Implications
47.5 Advancements in Adversarial ML
47.6 AI-powered Autonomous Attackers
47.7 Societal Impacts
47.8 Legal and Regulatory Trends
47.9 Future Skills for AI Attackers
47.10 Preparing for the Future
Lesson 48: Ethical and Legal Considerations
48.1 Offensive AI Ethics Overview
48.2 Legal Frameworks
48.3 Responsible Disclosure
48.4 AI in Red Team Engagements
48.5 Societal Implications
48.6 AI Misuse Prevention
48.7 Cross-border Legal Issues
48.8 Privacy and Data Protection
48.9 Ethical Hacking Guidelines
48.10 Case Studies
Lesson 49: Defensive Countermeasures to Offensive AI
49.1 Defense against AI-powered Attacks
49.2 AI-based Detection and Response
49.3 Model Robustness Techniques
49.4 Adversarial Training
49.5 Data Poisoning Prevention
49.6 AI in Threat Hunting
49.7 Blue Team Automation
49.8 Integrating Threat Intelligence
49.9 Legal and Policy Countermeasures
49.10 Case Studies
Lesson 50: Capstone – Offensive AI in Practice
50.1 Capstone Project Introduction
50.2 Scenario Selection
50.3 Offensive AI Tool Setup
50.4 Automated Reconnaissance
50.5 Vulnerability Discovery
50.6 Attack Execution
50.7 Evasion Techniques
50.8 Reporting and Lessons Learned
50.9 Defensive Recommendations
50.10 Capstone Presentation