Advanced Malware and Network Anomaly Detection

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Advanced Malware and Network Anomaly Detection

本课程是 AI for Cybersecurity 专项课程的一部分

位教师：Lanier Watkins

3,393 人已注册

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4个模块

深入了解一个主题并学习基础知识。

22 条评论

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

22 条评论

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

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.

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作业

9 项作业

授课语言：英语（English）

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本课程是 AI for Cybersecurity 专项课程专项课程的一部分

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该课程共有4个模块

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.

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.

涵盖的内容

2篇阅读材料

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.

涵盖的内容

2个视频3篇阅读材料3个作业

2个视频总计8分钟

Malware Threat Detection with AI 5分钟
Malware Analysis for Windows OS 3分钟

3篇阅读材料总计70分钟

Reading References 15分钟
Reading References 15分钟
Self-Reflective Reading: Analyzing PE Files- Insights and Reflections 40分钟

3个作业总计90分钟

Introduction to Malware Threat Detection Using AI 15分钟
Basic Malware Analysis for Windows Operating Systems 15分钟
Malware Threats Detection Part 1 60分钟

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.

涵盖的内容

2个视频3篇阅读材料3个作业1个非评分实验室

2个视频总计17分钟

Malware Threat Detection with Clustering and Decision Trees 7分钟
Metamorphic Malware Detection 10分钟

3篇阅读材料总计70分钟

Reading References 15分钟
Reading References 15分钟
Self-Reflective Reading: Deep Dive into AI and Malware Detection 40分钟

3个作业总计90分钟

Advanced Malware Detection with Clustering and Decision Trees 15分钟
Techniques for Metamorphic Malware Detection 15分钟
Malware Threats Detection Part 2 60分钟

1个非评分实验室总计60分钟

Practice Lab: SMS Spam Collection Using Naïve Bayes Spam Filter 60分钟

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.

涵盖的内容

2个视频4篇阅读材料3个作业1个非评分实验室

2个视频总计15分钟

Network Anomaly Detection 6分钟
Anomaly Detection: Botnet Case Study 9分钟

4篇阅读材料总计130分钟

Reading References 30分钟
Reading References 30分钟
Optional Reading 30分钟
Self-Reflective Reading: Unveiling the Essentials of Network Anomaly Detection with AI 40分钟

3个作业总计90分钟

Introduction to Network Anomaly Detection 15分钟
Anomaly Detection with Botnet Data: A Case Study 15分钟
Network Anomaly Detection with AI 60分钟

1个非评分实验室总计60分钟

Practice Lab: Building & Training an HMM for Metamorphic Malware Detection 60分钟

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位教师

授课教师评分

(7个评价)

Lanier Watkins

Johns Hopkins University

3 门课程 10,174 名学生

提供方

Johns Hopkins University

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已于 Oct 7, 2025审阅

I love it so much the way that course is designed and prepared hands on practice.

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