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

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 weeks to complete

at 10 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.

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Assessments

13 assignments

Taught in English

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This course is part of the Intrusion Detection Specialization

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There are 5 modules in this course

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.

Additionally, the course examines Tor networking, providing insights into secure and anonymous communication systems, as well as the critical role of IDS within Cyber Security Incident Response Teams (CSIRTs) in enterprise environments. By the end of the course, learners will not only understand how to apply advanced machine learning techniques but also be proficient in tools like RapidMiner and Security Onion. This blend of theory and hands-on application ensures that participants leave with the skills needed to tackle real-world cybersecurity challenges effectively, making this course a vital resource for those looking to advance their careers in cybersecurity and data science.

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

5 videosTotal 68 minutes

Introduction2 minutes
Introduction to Machine Learning Concepts 20 minutes
Neural Networks16 minutes
Clustering14 minutes
Help Vector Machines16 minutes

3 readingsTotal 220 minutes

Reading References90 minutes
Reading References90 minutes
Self-Reflective Reading: AI, Ethics, and Military Collaboration40 minutes

3 assignmentsTotal 90 minutes

Machine Learning I60 minutes
Introduction to Machine Learning and Data Mining15 minutes
Implementing and Evaluating IBM Watson for Fraud Detection15 minutes

2 ungraded labsTotal 120 minutes

Practice Lab: Building and Training a Neural Network with Keras60 minutes
Practice lab: Implementing and Tuning a Perceptron in Python60 minutes

3 pluginsTotal 43 minutes

Assurance for Machine Learning Video15 minutes
RapidMiner Example15 minutes
Deep Learning Using Keras – Training Neural Network13 minutes

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

3 videosTotal 27 minutes

Introduction3 minutes
Choosing an ML Algorithm5 minutes
Applying ML to ID20 minutes

4 readingsTotal 360 minutes

Reading References120 minutes
Reading References120 minutes
Self-Reflective Reading: Exploring Machine Learning in Intrusion Detection Systems60 minutes
Self-Reflective Reading: Analyzing Netflow Data and Machine Learning Models in Cybersecurity60 minutes

3 assignmentsTotal 90 minutes

Types of Machine Learning Algorithms and their Application in IDS15 minutes
Challenges in Implementing ML in IDS and Evaluation Techniques15 minutes
Machine Learning II60 minutes

5 pluginsTotal 63 minutes

Enigma Talk by Jeremy Howard on Deep Learning (Optional)20 minutes
Data Preparation9 minutes
Applying the Model7 minutes
Building the Model19 minutes
Validating a Model8 minutes

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

5 readingsTotal 410 minutes

Reading References120 minutes
Reading References120 minutes
ToR History & Data Anonymization90 minutes
Self-Reflective Reading: Exploring Neural Networks and Intrusion Detection in ToR Networks40 minutes
Self-Reflective Reading: Understanding Tor Network Anonymity and Security40 minutes

3 assignmentsTotal 90 minutes

ToR Networking60 minutes
Introduction to ToR Architectures and Node Types15 minutes
ToR Relays, Security Concerns, and Data Anonymization15 minutes

2 pluginsTotal 37 minutes

ToR Networking Video35 minutes
DJ Ware - Discussion of "The Onion Router" ToR2 minutes

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

7 videosTotal 127 minutes

Introduction4 minutes
Forming a CSIRT23 minutes
IDS Response Process28 minutes
Information Sharing20 minutes
Future Applications of IDS/IPS14 minutes
Technical Challenges Yet to Be Resolved23 minutes
Where to Get Information on the Future of IDS/IPS?16 minutes

7 readingsTotal 300 minutes

Reading References60 minutes
Concrete Steps for Implementing an Information Security Program20 minutes
Crisis Communications During a Security Incident20 minutes
Reading References60 minutes
Reading References60 minutes
Self-Reflective Reading: Analyzing the Cyber Intelligence Sharing and Protection Act (CISPA)40 minutes
Self-Reflective Reading: Integrating IDS and CSIRT in Cybersecurity and Evaluating CISA40 minutes

4 assignmentsTotal 105 minutes

IDS in Context60 minutes
Forming and Managing a CSIRT15 minutes
Executing IDS Response Processes with Security Onion15 minutes
Emerging Trends and Future Challenges in Intrusion Detection and Prevention Systems (IDS/IPS)15 minutes

4 pluginsTotal 176 minutes

Learning Rules for Anomaly Detection 60 minutes
The New Fundamentals of Security - Mike Fey - RSA Conference US 2013 Keynote28 minutes
Big Data Redefines Security - Arthur Coviello, Jr. - RSA Conference US 2013 Keynote (14E)31 minutes
Tech Talk on How to Detect Intruders Already in Your System 57 minutes

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Instructor

Jason Crossland

Johns Hopkins University

9 Courses6,887 learners

Offered by

Johns Hopkins University

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