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.

社会科学家长期以来一直对黑客的动机感兴趣，尤其是出于经济动机的攻击者。本文分析了网络破坏，这是一种较少研究的，更为公共的网络攻击形式，其中，网页内容被故意替换为犯罪者选择的不需要的文本和图形。这些攻击使用各种策略，并且出于各种动机而执行，包括政治和意识形态目标。这种攻击的激增导致大量数据的出现，为定性和定量分析提供了新的机会。本文探讨了机器学习技术对更好地了解攻击者策略和动机的有用性。为了检测总体攻击方式，此分析使用了40个样本，通过深度机器学习方法分析了污损网站上发布的000张图像。该方法展示了机器学习方法研究网络攻击的潜力，但同时也揭示了需要克服的巨大挑战。