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Erschienen in:
Situational Awareness, Sensemaking, and Situation Understanding in Cyber Warfare Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

6. Dynamic Logic Machine Learning for Cybersecurity

verfasst von : Leonid Perlovsky, Olexander Shevchenko

Erschienen in: Cybersecurity Systems for Human Cognition Augmentation

Verlag: Springer International Publishing

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Abstract

Today’s networks and their users are under attack from an ever-expanding universe of threats and malware. Malware are malicious software codes that typically damage or disable, take control of, or steal information from a computer system. Malware broadly includes botnets, viruses, worms, Trojan horses, logic bombs, rootkits, boot kits, backdoors, spyware, adware, and other types of threats. The ever increasing danger of the future threat is its ability to evolve for avoiding system defenses. Future threats will be using machine learning to outsmart the defenses. Defense techniques will in turn learn new attackers tricks to defend against. Therefore the future of cybersecurity is a warfare of machine learning techniques. The more capable machine learning technique will win.

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Metadaten
Titel
Dynamic Logic Machine Learning for Cybersecurity
verfasst von
Leonid Perlovsky
Olexander Shevchenko
Copyright-Jahr
2014
Buch
Cybersecurity Systems for Human Cognition Augmentation

Print ISBN: 978-3-319-10373-0

Electronic ISBN: 978-3-319-10374-7

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-10374-7_6

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