What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of AI for Cybersecurity Specialization
Instructor: Lanier Watkins
Included with
Recommended experience
Intermediate level
A foundational understanding of cybersecurity principles, and familiarity with basic programming concepts and machine learning are recommended.
Recommended experience
Intermediate level
A foundational understanding of cybersecurity principles, and familiarity with basic programming concepts and machine learning are recommended.
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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September 2024
9 assignments
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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.
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.
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.
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.
2 videos4 readings3 assignments1 ungraded lab
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