Johns Hopkins University
Advanced Malware and Network Anomaly Detection
Johns Hopkins University

Advanced Malware and Network Anomaly Detection

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

11 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace

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.

Details to know

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Recently updated!

September 2024

Assessments

9 assignments

Taught in English

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This course is part of the AI for Cybersecurity Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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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.

What's included

2 readings

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.

What's included

2 videos3 readings3 assignments

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.

What's included

2 videos3 readings3 assignments1 ungraded lab

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.

What's included

2 videos4 readings3 assignments1 ungraded lab

Instructor

Lanier Watkins
Johns Hopkins University
3 Courses34 learners

Offered by

Recommended if you're interested in Security

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