Designing a Machine Learning Intrusion Detection System: : Defend Your Network from Cybersecurity Threats / Tsukerman, Emmanuel.

Format:

Video

Author/Creator:

Tsukerman, Emmanuel, author.

Language:

English

Subjects (All):

Computer networks--Security measures.

Computer networks.

Intrusion detection systems (Computer security).

Machine learning.

Genre:

Electronic videos.

Physical Description:

1 online resource (1 video file, approximately 53 min.)

Edition:

1st edition

Place of Publication:

Apress, 2020.

System Details:

video file

Summary:

This video will guide you on the principles and practice of designing a smart, AI-based intrusion detection system (IDS) to defend a network from cybersecurity threats. The course begins by explaining the theory and state of the art of the field, and then proceeds to guide you on the step-by-step implementation of an ML-based IDS. The first part of the course will explain how an intrusion detection system is used to stop cybersecurity threats such as hackers from infiltrating your network. Next, it will explain why traditional intrusion detection systems are not able to keep up with the rapid evolution of black hat adversaries, and how machine learning offers a self-learning solution that is able to keep up with, and even outsmart them. Further, you will learn the high-level architecture of an ML-based IDS; how to carry out data collection, model selection, and objective selection (such as accuracy or false positive rate); and how all these come together to form a next-generation IDS. Moving forward, you'll see how to implement the ML-based IDS. What You Will Learn Discover how an IDS works See how machine learning-based IDSs are able to solve the problems that traditional IDSs have faced Architect a machine learning-based IDS Train the ML components of a next-generation IDS Choose the correct metric function for your next-generation IDS in order to satisfy the most commonly encountered business objectives Who This Video Is For Cybersecurity professionals, data scientists, and students of these disciplines.

Participant:

Presenter, Emmanuel Tsukerman.

Notes:

Online resource; Title from title screen (viewed September 29, 2020)

Title from resource description page (Safari, viewed December 7, 2020).

OCLC:

1226354836