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Machine Learning for Cyber Defense and Attack

Authors:
Manjeet Rege
Raymond Blanch K. Mbah

Keywords: Cyber Security; Machine Learning; Malware; Thread Detection and Classification; Network Risk Scoring.

Abstract:
The exponential advancements in processing, storage and network technologies have led to the recent explosive growth in big data, connectivity and machine learning. The world is becoming increasingly digitalized - raising security concerns and the desperate need for robust and advanced security technologies and techniques to combat the increasing complex nature of cyber-attacks. This paper discusses how machine learning is being used in cyber security in both defense and offense activities, including discussions on cyber-attacks targeted at machine learning models. Specifically, we discuss the applications of machine learning in carrying out cyber-attacks, such as in smart botnets, advanced spear fishing and evasive malwares. We also explain the application of machine learning in cyber security, such as in threat detection and prevention, malware detection and classification, and network risk scoring.

Pages: 73 to 78

Copyright: Copyright (c) IARIA, 2018

Publication date: November 18, 2018

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-681-1

Location: Athens, Greece

Dates: from November 18, 2018 to November 22, 2018