The course "Machine Learning and Emerging Technologies in Cybersecurity" offers an in-depth exploration of machine learning applications in cybersecurity, focusing on techniques for threat detection and prevention. Participants will gain a solid grounding in machine learning fundamentals, including neural networks, clustering, and support vector machines, tailored specifically for cybersecurity contexts. Unique to this course is the integration of machine learning with Intrusion Detection Systems (IDS), equipping learners with practical skills to enhance threat detection capabilities.
Machine Learning and Emerging Technologies in Cybersecurity
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Machine Learning and Emerging Technologies in Cybersecurity
This course is part of Intrusion Detection Specialization
Instructor: Jason Crossland
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
39 hours to complete
3 weeks at 13 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Explore advanced machine learning techniques, including neural networks and clustering, for improved threat detection in cybersecurity.
Understand the integration of machine learning algorithms into Intrusion Detection Systems (IDS) for enhanced security measures.
Gain knowledge of The Onion Router (ToR) architecture and its applications, focusing on privacy and anonymous communication.
Learn to utilize Security Onion tools for effective incident response within high-volume enterprise environments, enhancing cybersecurity strategy.
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
November 2024
Assessments
13 assignments
Taught in English
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This course is part of the Intrusion Detection Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 5 modules in this course
This course provides a comprehensive introduction to machine learning and data mining, covering key algorithms and tools like RapidMiner and Security Onion. Students will explore advanced topics such as neural networks, clustering, and support vector machines, while also learning to evaluate model performance through confusion matrices and ROC curves. Additionally, the course delves into ToR architecture, privacy concerns, and the practical installation of ToR clients. Emphasis will be placed on incident response within Computer Security Incident Response Teams (CSIRTs) and effective information-sharing practices. By the end of the course, participants will have a robust understanding of both machine learning techniques and their applications in cybersecurity.
What's included
1 video3 readings
The course delves deeper into specific approaches, including neural networks, clustering, and support vector machines (SVMs), providing students with a solid foundation in both the theory and practice of these advanced techniques.
What's included
5 videos3 readings3 assignments2 ungraded labs3 plugins
This course explores the integration of Machine Learning (ML) algorithms into Intrusion Detection Systems (IDS) to enhance threat detection capabilities.
What's included
3 videos4 readings3 assignments5 plugins
This course provides a comprehensive understanding of The Onion Router (ToR) architectures, focusing on the critical components that make up this secure and anonymous communication system.
What's included
5 readings3 assignments2 plugins
This module explores the critical role of Intrusion Detection Systems (IDS) within Cyber Security Incident Response Teams (CSIRTs), particularly in high-volume enterprise environments.
What's included
7 videos7 readings4 assignments4 plugins
Instructor
Offered by
Recommended if you're interested in Security
Johns Hopkins University
Johns Hopkins University
EC-Council
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