Advanced Malware and Network Anomaly Detection

Instructor: Lanier Watkins

Intermediate Level • 11 hours to complete 3 weeks at 3 hours a week • Flexible Schedule

What You'll Learn

  • Understand various types of malware 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 analyze network traffic for unusual patterns.
  • Collaborate and present research findings on current trends in network anomaly detection, enhancing communication and analytical skills.

Skills You'll Gain

Supervised Learning
Threat Detection
Machine Learning Methods
Network Analysis
System Design and Implementation
Network Security
Malware Protection
Cybersecurity
Machine Learning Algorithms
Intrusion Detection and Prevention
Machine Learning
Continuous Monitoring
Machine Learning Software
Anomaly Detection
Microsoft Windows
Performance Testing

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

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.

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.

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.