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Dynamic Logic Machine Learning for Cybersecurity

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Cybersecurity Systems for Human Cognition Augmentation

Part of the book series: Advances in Information Security ((ADIS,volume 61))

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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Author information

Authors and Affiliations

  1. LP Information Technology & Harvard University, Cambridge, MA, USA

    Leonid Perlovsky

  2. LP Information Technology, Cambridge, MA, USA

    Olexander Shevchenko

Authors
  1. Leonid Perlovsky
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  2. Olexander Shevchenko
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Correspondence to Leonid Perlovsky .

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Editors and Affiliations

  1. ICF International, Fairfax, Virginia, USA

    Robinson E. Pino

  2. Army Research Laboratory, Adelphi, Maryland, USA

    Alexander Kott

  3. ICF International, Darlington, Maryland, USA

    Michael Shevenell

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Perlovsky, L., Shevchenko, O. (2014). Dynamic Logic Machine Learning for Cybersecurity. In: Pino, R., Kott, A., Shevenell, M. (eds) Cybersecurity Systems for Human Cognition Augmentation. Advances in Information Security, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-319-10374-7_6

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

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