Securing AI systems against manipulation, bias, and adversarial attacks
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.
Advanced undergraduate students, graduate students, cybersecurity and AI professionals
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.
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.
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.
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.
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.
Upon successful completion, learners will demonstrate: