Advanced Malware and Network Anomaly Detection
Master AI-powered malware analysis and network threat detection using machine learning algorithms and anomaly detection techniques.
Master AI-powered malware analysis and network threat detection using machine learning algorithms and anomaly detection techniques.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full AI for Cybersecurity Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Apply AI techniques for malware detection and classification
Implement machine learning algorithms for threat detection
Analyze network anomalies using botnet data
Develop advanced malware analysis skills
Evaluate detection system performance
Skills you'll gain
This course includes:
0.7 Hours PreRecorded video
9 assignments, 2 ungraded labs
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course combines advanced malware detection techniques with network anomaly identification using artificial intelligence. The curriculum covers various types of malware, analysis tools, and both supervised and unsupervised machine learning methods for threat detection. Students gain hands-on experience through practical implementations, including botnet data analysis and metamorphic malware detection, while developing skills in performance evaluation and research presentation.
Course Introduction
Module 1 · 7 Minutes to complete
Malware Threats Detection Part 1
Module 2 · 2 Hours to complete
Malware Threats Detection Part 2
Module 3 · 3 Hours to complete
Network Anomaly Detection with AI
Module 4 · 4 Hours to complete
Fee Structure
Instructor
Cybersecurity Leader and Chair of Computer Science Programs at Johns Hopkins University
Lanier Watkins is a distinguished academic and leader in the field of cybersecurity, currently serving as the chair of the Engineering for Professionals Master’s programs in Computer Science and Cybersecurity at Johns Hopkins University. He also holds a secondary appointment as an associate research professor at the JHU Information Security Institute, where he contributes as the assistant technical director. With over ten years of industry experience at Ford Motor Company and AT&T, Lanier has developed innovative algorithms and frameworks aimed at defending critical infrastructure networks against evolving cyber threats. His research focuses on key areas such as network security, Internet of Things (IoT) security, vulnerability monitoring, malware analysis, and data analytics.In addition to his administrative and research roles, Lanier Watkins teaches several courses on Coursera, including "Introduction to AI for Cybersecurity" and "Advanced Malware and Network Anomaly Detection." His work has been recognized with numerous awards, including the Black Engineer of the Year’s Modern-Day Technology Leader Award. A senior member of the IEEE and a member of the Association for Computing Machinery, he has published extensively in top-tier journals and holds multiple patents related to cybersecurity technologies. His commitment to advancing knowledge in cybersecurity not only enhances academic discourse but also contributes significantly to practical applications in the field.
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Frequently asked questions
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