Advanced Malware and Network Anomaly Detection

Johns Hopkins University via Coursera

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Overview

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 youll explore anomaly detection algorithms and their...

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.