Cybersecurity
Academy Introduction to Cybersecurity Information Security Principles Cyber Threats, Attacks, and Defense Mechanisms Network Security Operating System & Endpoint Security Cryptography and Data Protection Ethical Hacking & Penetration Testing Application Security Digital Forensics & Incident Response Cloud Security Governance, Risk, and Compliance (GRC) Cybersecurity Management & Strategy AI Security and Adversarial Machine Learning
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Samer
AI Security and Adversarial Machine Learning
Securing AI systems against manipulation, bias, and adversarial attacks

Available Feb 2026 English 0.00

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Course Description

This course focuses on the security risks, vulnerabilities, and attack surfaces introduced by artificial intelligence and machine learning systems. Students examine how AI models can be manipulated through adversarial inputs, data poisoning, model inversion, and membership inference attacks. The course also addresses AI-specific risk management, model robustness, explainability, and governance considerations. Emphasis is placed on securing AI pipelines across data collection, training, deployment, and monitoring, while balancing ethical, legal, and operational concerns. The course prepares learners to evaluate and defend AI-enabled systems in high-risk and mission-critical environments.



Learning Outcomes
Upon completion of Introduction to Information Security, students will be able to:
  1. Demonstrate an understanding of the principles and processes of digital forensics and its role in investigating digital crime
  2. Perform tasks to identify and preserve digital evidence and conduct a thorough digital forensic examination
  3. Perform tasks to analyze nd interpret digital forensic data using various tools and techniques
  4. Communicate findings in a clear and comprehensive manner
Prerequisite Knowledge: Cloud Security or Cyber Threats, Attacks, and Defense Mechanisms
Requirements: TBA
Duration: 42 hours
Files: Kali Linux, Windows 10 Target, Metasploitable 2
Course Access
Digital Book
Read
Full access to digital learning materials              
YouTube Channel
Watch
Dedicated YouTube Playlist for web application security
Apply
Apply
Apply your knowledge in a contrlled lab environment    
Course Outline
Target Audience

Advanced undergraduate students, graduate students, cybersecurity and AI professionals

Edition

2026

Course Modules

Module 1 - Introduction to Digital Forensics: This module provides an overview of digital forensics and its importance in investigating digital crime. It covers the principles and processes of digital forensics and the different types of digital evidence.

Module 2 - Digital Evidence Collection and Preservation: This module covers the techniques for collecting and preserving digital evidence, including the use of imaging and hashing techniques. It also covers the legal issues involved in the collection and preservation of digital evidence.

Module 3 - Digital Forensics Examination: This module covers the techniques and tools used to conduct a digital forensic examination, including the use of forensic software and hardware tools. It also covers the analysis of different types of digital evidence, such as computer, mobile, and network forensics.

Module 4 - Reporting and Presenting Findings: This module will cover the process of reporting and presenting digital forensic findings. It will include the use of report writing software and the creation of visual aids to present findings in a clear and comprehensive manner.

Module 5 - Advanced Topics: This module will cover more specialized areas within digital forensics, such as cloud forensics and blockchain forensics. It will also cover emerging trends in digital forensics, such as Artificial Intelligence and Machine Learning applications in digital forensics.

Legal

Some of the product names and company names used in this course have been used for identification purposes only and may be trademarks or registered trademarks of their respective organizations. The software tools and applications in this course are for instructional purposes only. They have been tested with care, but are not guaranteed for any particular intent beyond educational purposes. The author does not offer any warranties or representations, nor does he accept any liabilities with respect to the programs.
© 2022-2023 Samer Aoudi

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AI Security and Adversarial Machine Learning