Using Machine Learning to Examine Cyberattack Motivations on Web Defacement Data

Abstract

Social scientists have long been interested in the motives of hackers, particularly financially motivated attackers. This article analyzes web defacements, a less studied and more public form of cyberattack, in which the content of a web page is deliberately substituted with unwanted text and graphics chosen by the perpetrator. These attacks use a variety of strategies and are performed for a variety of motives, including political and ideological goals. The proliferation of such attacks has resulted in vast amounts of data that open new opportunities for qualitative and quantitative analysis. This article explores the usefulness of machine learning techniques to better understand attacker strategies and motivations. To detect overall attack patterns, this analysis utilized a sample of 40,000 images posted on defaced websites analyzed through deep machine learning methods. The approach demonstrates the potential of machine learning approaches for the study of cyberattacks, but it also reveals the considerable challenges that need to be overcome.