Johns Hopkins University
Machine Learning and Emerging Technologies in Cybersecurity
Johns Hopkins University

Machine Learning and Emerging Technologies in Cybersecurity

This course is part of Intrusion Detection Specialization

Jason Crossland

Instructor: Jason Crossland

Included with Coursera Plus

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
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.

Details to know

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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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
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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

Jason Crossland
Johns Hopkins University
6 Courses362 learners

Offered by

Recommended if you're interested in Security

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