Fundamentals of Machine Learning (ML) and Artificial Intelligence (AI) in Offensive Security Essentials Training by Tonex
This course introduces the foundational principles of machine learning (ML) and artificial intelligence (AI) applied to offensive security. Participants will explore how ML and AI can be leveraged to enhance penetration testing, vulnerability analysis, and exploit development. Through real-world scenarios, attendees will learn to use AI tools and frameworks to automate, optimize, and innovate in offensive security strategies.
Learning Objectives
By the end of this course, participants will be able to:
- Understand the basics of machine learning and artificial intelligence, including key algorithms and models.
- Explore the role of ML and AI in offensive security, including ethical hacking and penetration testing.
- Use ML techniques to identify vulnerabilities, predict exploits, and generate attack strategies.
- Develop and deploy adversarial machine learning techniques to test AI-based defensive systems.
- Automate reconnaissance and attack planning using AI tools.
- Analyze real-world case studies to understand the impact of AI in cybersecurity attacks.
Course Modules
Day 1: Foundations of ML and AI in Offensive Security
Session 1: Introduction to ML and AI (1 hour)
- Basics of machine learning: Supervised, unsupervised, and reinforcement learning.
- Overview of artificial intelligence and its applications in security.
- AI and ML tools and frameworks: TensorFlow, PyTorch, and Scikit-learn.
Session 2: Offensive Security Basics (1 hour)
- Key concepts in penetration testing and ethical hacking.
- The role of automation and intelligence in offensive security.
- Introduction to attack vectors: Reconnaissance, exploitation, and persistence.
Session 3: ML Algorithms for Offensive Security (2 hours)
- Feature engineering for vulnerability detection.
- Predictive modeling for attack surface analysis.
- Hands-on lab: Building a basic ML model to classify vulnerabilities.
Break: 30 minutes
Session 4: AI-Driven Reconnaissance and Exploitation (2 hours)
- Automating reconnaissance with NLP (Natural Language Processing).
- Using AI to identify weak points in network configurations.
- Hands-on lab: Implementing an AI-based reconnaissance tool.
Wrap-Up Discussion (30 minutes)
- Q&A and participant feedback.
Day 2: Advanced Applications and Techniques
Session 1: Adversarial Machine Learning (1.5 hours)
- What is adversarial machine learning?
- Techniques for generating adversarial examples.
- Hands-on lab: Building adversarial attacks to evade AI-based defenses.
Session 2: Automating Attack Planning with AI (1.5 hours)
- AI for attack path prediction and prioritization.
- Reinforcement learning for decision-making in offensive security.
- Hands-on lab: Developing an AI-based attack planner.
Break: 30 minutes
Session 3: Case Studies in AI and Offensive Security (2 hours)
- AI-based cyber attacks: Examples from the wild.
- How attackers exploit AI and ML systems.
- Analyzing real-world incidents involving AI-driven attacks.
Session 4: Ethical Considerations and Future Trends (1.5 hours)
- Ethics of using AI in offensive security.
- Regulatory and legal implications.
- The future of AI in offensive and defensive cybersecurity.
Wrap-Up Panel Discussion (1 hour)
- Expert panel: Balancing offensive and defensive AI strategies.
- Open Q&A and participant feedback.
Key Features
- Real-World Case Studies: Analysis of AI-driven cyber attacks and vulnerabilities.
- Interactive Discussions: Explore ethical dilemmas and future implications.
- Certificate of Completion: Recognizing participants’ achievements in the course.