VW Intermediate Levels AI for Cybersecurity
• Malware Threats Detection Part 1
In this module, we will discuss common types of malwares, malware analysis tools, and basic
malware analysis processes. Specifically, we will be discussing basic approaches to analysing
Windows-based malware.
• Malware Threats Detection Part 2
In this module, we investigate hands-on malware detection implementations, both unsupervised and supervised. Also, we discuss metrics to evaluate the performance of malware detection algorithms.
• Advanced Malware and Network Anomaly Detection
Understand various types of malwares and apply foundational analysis techniques to effectively detect and classify them.
Implement advanced machine learning algorithms, including clustering and decision trees, for efficient malware detection.
Explore anomaly detection techniques using botnet data and learn how to analyse network traffic for unusual patterns.
Collaborate and present research findings on current trends in network anomaly detection, enhancing communication and analytical skills.
• Securing AI and Advanced Topics
Learn to implement AI-based solutions to detect and prevent credit card fraud in cloud environments.
Explore the fundamentals of Generative Adversarial Networks and their applications in generating synthetic data.
Gain hands-on experience with black-box and white-box adversarial attacks to assess and enhance model resilience.
Master techniques in feature engineering and performance evaluation to optimize AI models for cybersecurity applications.