Advanced Malware and Network Anomaly Detection

Overview

Class Central Tips

The course "Advanced Malware and Network Anomaly Detection" equips learners with essential skills to combat advanced cybersecurity threats using artificial intelligence. This course takes a hands-on approach, guiding students through the intricacies of malware detection and network anomaly identification. In the first two modules, you will gain foundational knowledge about various types of malware and advanced detection techniques, including supervised and unsupervised learning methods. The subsequent modules shift focus to network security, where you’ll explore anomaly detection algorithms and their application using real-world botnet data. What sets this course apart is its emphasis on practical, project-based learning. By applying your knowledge through hands-on implementations and collaborative presentations, you will develop a robust skill set that is highly relevant in today’s cybersecurity landscape. Completing this course will prepare you to effectively identify and mitigate threats, making you a valuable asset in any cybersecurity role. With the rapid evolution of cyber threats, this course ensures you stay ahead by leveraging the power of AI for robust cybersecurity measures.

Syllabus

Course Introduction

This course provides a comprehensive exploration of malware detection and analysis, covering the identification and classification of malware types and their characteristics. Students will learn fundamental concepts of malware analysis, network threats, and detection methods while employing various tools and algorithms for effective detection and performance assessment.

Malware Threats Detection Part 1

In this module, we will discuss common types of malware, malware analysis tools, and basic malware analysis processes. Specifically, we will be discussing basic approaches to analyzing 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.

Network Anomaly Detection with AI

This module will discuss the background of network threats and anomaly detection. Also, we explore hands-on implementations of anomaly detection analytics using botnet data and the next evolution of anomaly detection, autonomic cybersecurity systems.