SN Video coding and web development. Designing a machine learning intrusion detection system / Springer.

Format:

Video

Contributor:

Tsukerman, Emmanuel, speaker.

Springer Nature (Firm), publisher.

Series:

Academic Video Online

Language:

English

Subjects (All):

Computer networks--Security measures.

Computer networks.

Intrusion detection systems (Computer security).

Machine learning.

Genre:

Instructional films.

Physical Description:

1 online resource (53 minutes)

Other Title:

Designing a machine learning intrusion detection system

Springer Nature video coding and web development

Place of Publication:

London, England : Springer Nature, 2020.

Language Note:

In English.

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.

Notes:

Title from resource description page (viewed March 15, 2021).

OCLC:

1245589958