Advanced Malware and Network Anomaly Detection

Advanced Malware and Network Anomaly Detection

This course is part of AI for Cybersecurity Specialization

Instructor: Lanier Watkins

Access provided by Assam down town University

3,037 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

19 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

19 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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.

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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

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.

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

2 videos Total 8 minutes

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

3 readings Total 70 minutes

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

3 assignments Total 90 minutes

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

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

2 videos Total 17 minutes

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

3 readings Total 70 minutes

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

3 assignments Total 90 minutes

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

1 ungraded lab Total 60 minutes

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

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

2 videos Total 15 minutes

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

4 readings Total 130 minutes

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