Home // CYBER 2018, The Third International Conference on Cyber-Technologies and Cyber-Systems // View article
Authors:
Thomas Klemas
Steve Chan
Keywords: artificial intelligence; expert systems, machine learning; supervised learning, unsupervised learning, pattern recognition, spectral methods, k-means, modularity, Lagrange multiplier, optimization, anomaly detection, data analytics, data science, networks
Abstract:
While media reports frequently highlight the exciting aspects of the cyber security field, many cyber security tasks are quite tedious and repetitive. At the same time, however, strong pattern recognition, deductive reasoning, and inference skills are required, as well as a high degree of situational awareness. As a direct consequence, the field of cyber security is replete with potential opportunities to apply data analytics, machine learning, computer aided testing, and other advanced approaches to reduce the frustration of cyber security operators by easing key challenges. In fact, given a typical range of cyber attack surfaces, leveraging these machine-enhanced analysis and decision approaches in conjunction with a robust defense-in-depth posture is a crucial step towards achieving sustained, predictable performance across typical cyber security tasks and promotes cyber resilience. This paper will both outline details for a near-term research effort and explore a variety of key opportunities to exploit these approaches with the objective of raising awareness, providing initial guidance to aid potential adopters, and developing effective strategies to incorporate them into existing cyber security constructs.
Pages: 63 to 67
Copyright: Copyright (c) IARIA, 2018
Publication date: November 18, 2018
Published in: conference
ISSN: 2519-8599
ISBN: 978-1-61208-683-5
Location: Athens, Greece
Dates: from November 18, 2018 to November 22, 2018