Machine Learning and Emerging Technologies in Cybersecurity

Machine Learning and Emerging Technologies in Cybersecurity

This course is part of Intrusion Detection Specialization

Instructor: Jason Crossland

Included with

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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.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

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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Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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 videos Total 68 minutes

Introduction 2 minutes
Introduction to Machine Learning Concepts 20 minutes
Neural Networks 16 minutes
Clustering 14 minutes
Help Vector Machines 16 minutes

3 readings Total 220 minutes

Reading References 90 minutes
Reading References 90 minutes
Self-Reflective Reading: AI, Ethics, and Military Collaboration 40 minutes

3 assignments Total 90 minutes

Machine Learning I 60 minutes
Introduction to Machine Learning and Data Mining 15 minutes
Implementing and Evaluating IBM Watson for Fraud Detection 15 minutes

2 ungraded labs Total 120 minutes

Practice Lab: Building and Training a Neural Network with Keras 60 minutes
Practice lab: Implementing and Tuning a Perceptron in Python 60 minutes

3 plugins Total 43 minutes

Assurance for Machine Learning Video 15 minutes
RapidMiner Example 15 minutes
Deep Learning Using Keras – Training Neural Network 13 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 videos Total 27 minutes

Introduction 3 minutes
Choosing an ML Algorithm 5 minutes
Applying ML to ID 20 minutes

4 readings Total 360 minutes

Reading References 120 minutes
Reading References 120 minutes
Self-Reflective Reading: Exploring Machine Learning in Intrusion Detection Systems 60 minutes
Self-Reflective Reading: Analyzing Netflow Data and Machine Learning Models in Cybersecurity 60 minutes

3 assignments Total 90 minutes

Types of Machine Learning Algorithms and their Application in IDS 15 minutes
Challenges in Implementing ML in IDS and Evaluation Techniques 15 minutes
Machine Learning II 60 minutes

5 plugins Total 63 minutes

Enigma Talk by Jeremy Howard on Deep Learning (Optional) 20 minutes
Data Preparation 9 minutes
Applying the Model 7 minutes
Building the Model 19 minutes
Validating a Model 8 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 readings Total 410 minutes

Reading References 120 minutes
Reading References 120 minutes
ToR History & Data Anonymization 90 minutes
Self-Reflective Reading: Exploring Neural Networks and Intrusion Detection in ToR Networks 40 minutes
Self-Reflective Reading: Understanding Tor Network Anonymity and Security 40 minutes

3 assignments Total 90 minutes

ToR Networking 60 minutes
Introduction to ToR Architectures and Node Types 15 minutes
ToR Relays, Security Concerns, and Data Anonymization 15 minutes

2 plugins Total 37 minutes

ToR Networking Video 35 minutes
DJ Ware - Discussion of "The Onion Router" ToR 2 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 videos Total 127 minutes

Introduction 4 minutes
Forming a CSIRT 23 minutes
IDS Response Process 28 minutes
Information Sharing 20 minutes
Future Applications of IDS/IPS 14 minutes
Technical Challenges Yet to Be Resolved 23 minutes
Where to Get Information on the Future of IDS/IPS? 16 minutes

7 readings Total 300 minutes

Reading References 60 minutes
Concrete Steps for Implementing an Information Security Program 20 minutes
Crisis Communications During a Security Incident 20 minutes
Reading References 60 minutes
Reading References 60 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 CISA 40 minutes

4 assignments Total 105 minutes

IDS in Context 60 minutes
Forming and Managing a CSIRT 15 minutes
Executing IDS Response Processes with Security Onion 15 minutes
Emerging Trends and Future Challenges in Intrusion Detection and Prevention Systems (IDS/IPS) 15 minutes

4 plugins Total 176 minutes

Learning Rules for Anomaly Detection 60 minutes
The New Fundamentals of Security - Mike Fey - RSA Conference US 2013 Keynote 28 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 Courses 6,122 learners

Offered by

Johns Hopkins University

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