Description
Machine Learning for Hackers is a course designed to equip cybersecurity professionals with the skills needed to both attack and defend machine learning systems. As ML technologies become integral to security solutions, understanding their vulnerabilities is crucial. This course begins with setting up an ML hacking lab and learning to interact with models using Python. Participants will map the ML attack surface and conduct end-to-end classifier attacks. The curriculum covers PE file structure and byte-level evasion, building and bypassing malware classifiers, and automating phishing and social engineering with language models. It also includes deepfake creation and detection, behavioral analysis, adversarial ML techniques, smart fuzzing for vulnerability discovery, and model stealing, poisoning, watermarking, and backdoors. By the end of the course, learners will be able to test and secure ML systems, craft and detect adversarial samples, and apply effective model defense strategies. The course is taught by Tshering Tobzang, a senior cybersecurity officer with expertise in adversarial machine learning and automated threat detection. This course is ideal for cybersecurity professionals looking to expand their expertise in ML-based security solutions.
- CategoryEducation & Research
- Pricing ModelSubscription
Machine Learning for Hackers's Core Features
Set up an ML hacking lab
Interact with ML models using Python
Map the ML attack surface
Conduct end-to-end classifier attacks
Modify PE files for evasion
Automate phishing with language models
Perform adversarial ML techniques
Explore model theft and backdoor attacks
How to use Machine Learning for Hackers?
Set up: Configure your ML hacking lab
Interact: Use Python to engage with ML models
Map: Identify the ML attack surface
Conduct: Execute end-to-end classifier attacks
Modify: Alter PE files to evade detection
Automate: Use language models for phishing
Perform: Conduct adversarial ML attacks
Explore: Investigate model theft and backdoors
Machine Learning for Hackers's Use Cases
- ML System Security
- Adversarial Attacks
- Phishing Automation
- Deepfake Detection
- Model Theft Prevention
