从Web源发现信号以预测网络攻击
Discovering Signals from Web Sources to Predict Cyber Attacks
摘要：
网络攻击的频率和严重性都在增加。仅在过去的一年里，我们就目睹了大规模的数据泄露事件，窃取了数百万人的个人信息，大规模的勒索软件攻击使几个国家的关键基础设施瘫痪。应对不断上升的网络威胁需要多管齐下的战略，其中包括预测这些攻击何时发生。驱动我们方法的直觉是这样的：在计划和准备阶段，黑客在表面网络和暗网上留下他们活动的数字痕迹，在黑客论坛，社交媒体，博客等平台上进行讨论。这些数据提供了预测信号，可以预测网络攻击。在本文中，我们描述了基于深度神经网络和自回归时间序列模型的机器学习技术，这些模型利用来自公开Web源的外部信号来预测网络攻击。我们的框架在真实数据中的表现表明，我们的方法对预测网络攻击的顶级信号产生了显著的提升或F1增加。我们的研究结果表明，当部署时，我们的系统将能够针对各种类型的有针对性的网络攻击提供有效的防线。
英文摘要：
Cyber attacks are growing in frequency and severity. Over the past year alone we have witnessed massive data breaches that stole personal information of millions of people and wide-scale ransomware attacks that paralyzed critical infrastructure of several countries. Combating the rising cyber threat calls for a multi-pronged strategy, which includes predicting when these attacks will occur. The intuition driving our approach is this: during the planning and preparation stages, hackers leave digital traces of their activities on both the surface web and dark web in the form of discussions on platforms like hacker forums, social media, blogs and the like. These data provide predictive signals that allow anticipating cyber attacks. In this paper, we describe machine learning techniques based on deep neural networks and autoregressive time series models that leverage external signals from publicly available Web sources to forecast cyber attacks. Performance of our framework across ground truth data over real-world forecasting tasks shows that our methods yield a significant lift or increase of F1 for the top signals on predicted cyber attacks. Our results suggest that, when deployed, our system will be able to provide an effective line of defense against various types of targeted cyber attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
