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The Truth About Machine Learning in Cybersecurity: Defense

Abstract: 
As we continue to see a rise in the number and severity of cyber attacks, we simultaneously have seen an explosion of progress in the field of artificial intelligence, and specifically machine learning. Machine learning is a sub-field of AI which thrives on the availability of data. With a sufficient amount of data, for example a large number of emails with phishing attempts, we can train an algorithm to be able to recognize a new phishing attempt as malicious. Naturally, with the growing problems in cybersecurity and the growing efficacy of machine learning algorithms which can make predictions from complex data, there has been interest in applying machine learning to security. This article explores different categories of machine learning such as classification, regression, and clustering to give examples of how they can be used in security. However, the article starts with a warning about how machine learning on its own will not solve our security problems. The author seeks to separate his overview of these techniques from the hype surrounding machine learning and its incredible promises. Keywords: Machine learning, regression, classification, clustering, forensic analysis
Author: 
Alexander Polyakov
Institution: 
Forbes
Year: 
2018
Input By: 
Molly Nagele
Domains-Issue Area: 
Dimensions-Problem/Solution: 
Region(s): 
Industry Focus: 
Internet & Cyberspace
Datatype(s): 
Models