Overview What You Will Learn Curriculum Instructor

Course Overview

As cybersecurity and Machine Learning (ML) technologies continue to grow rapidly, it has become essential for both attackers and defenders to understand the intersection of these fields. With ML being integrated into various security solutions and decision-making processes, understanding how it works and how it can be manipulated is crucial for staying ahead in the cybersecurity landscape. Understanding ML models, APIs, and data pipelines is critical because attackers exploit their weaknesses and defenders must harden them. This course shows how ML can both empower sophisticated attacks and provide powerful detection and mitigation when used responsibly. 

 This course begins with setting up an ML hacking lab and learning to interact with models using Python, then maps the ML attack surface and runs an end-to-end classifier attack. It then covers PE file structure and byte-level evasion, building and bypassing malware classifiers, automating phishing and social engineering with language models, deepfake creation and detection, behavioral analysis, adversarial ML techniques including white-box and black-box attacks and API targeting, smart fuzzing for vulnerability discovery and evasion, and model stealing, poisoning, watermarking, backdoors, and red-team uses of stolen models. 

By the end of this course, you will be equipped to test and secure ML systems, craft and detect adversarial samples, uncover ML vulnerabilities, and apply effective model defense strategies.

What You Will Learn

  • How to attack and defend machine learning systems using practical tools.
  • How to build and train ML-based malware classifiers and break them using adversarial techniques.
  • Smart fuzzing and code coverage to find vulnerabilities.
  • How to modify PE files at the byte level to evade static malware classifiers.
  • Understand and implement social engineering via ML-driven manipulation.
  • Perform white-box and black-box adversarial attacks.
  • Explore model theft and backdoor attacks

Program Curriculum

  • Set Up Your ML Hacking Lab
  • Learn to Interact with ML Models Using Python
  • Understand the Attack Surface of Machine Learning Systems
  • Identify Common Vulnerabilities in ML Systems
  • Conduct Your First End-to-End Attack on an ML Classifier
  • Chapter 1 Quiz

  • Introduction to PE File Structure and Evasion Tactics
  • Modify PE Files with Byte-Level Perturbations
  • Build Your First Malware Classifier Using ML
  • Evade Static Malware Detection with Minimal Changes
  • Create Adversarial Malware Samples to Evade Detection
  • Chapter 2 Quiz

  • Automate Phishing with Language Models
  • Use NLP for Social Engineering in Cybersecurity
  • Create Deepfakes to Manipulate Targets
  • Simulate Social Engineering Attacks using ML-Generated Content
  • Detect Social Engineering via Behavioral Analysis
  • Chapter 3 Quiz

  • Introduction to Adversarial Machine Learning
  • White-Box Attacks: Understanding Model Internals
  • Black-Box Attacks: Attacking Without Model Knowledge
  • Attacking Clarifai with Adversarial Examples
  • Understand Defenses Against Adversarial Attacks in ML Models
  • Chapter 4 Quiz

  • Introduction to Smart Fuzzing
  • Automating Attack Path Discovery with Smart Fuzzing
  • Code Coverage in Fuzzing
  • Evading Detection Using Smart Fuzzing
  • Chapter 5 Quiz

  • Steal Machine Learning Models via API Query Attacks
  • Detect and Neutralize Poisoned Machine Learning Models
  • Implement Watermarks to Protect Against Model Theft
  • Simulate Red Team Attacks Using Stolen ML Models
  • Embed Hidden Backdoors in ML Models for Exploitation
  • Chapter 6 Quiz
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Instructor

Tshering Tobzang

Tshering Tobzang is a senior cybersecurity officer with a master’s degree in Cybersecurity and Digital Forensics. He specializes in applying machine learning in cybersecurity, including adversarial machine learning, automated threat detection, and deep learning techniques used in red and blue team operations.

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