Understanding Cyber Attack Behaviors with Sentiment Information on Social Media

Shu, K.2, Sliva, A.1, Sampson, J.2, and Liu, H.2

Presented at the 2018 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation (SBP-BRiMS), Washington DC, USA (July 2018)

In today’s increasingly connected world, cyber attacks have become a serious threat with detrimental effects on individuals, businesses,and broader society. Truly mitigating the negative impacts of these attacks requires a deeper understanding of malicious cyber activities and the capability of predicting these attacks before they occur. However, detecting the occurrence of cyber attacks is non-trivial due to the anonymity of cyber attacks and the ambiguity or unavailability of network data collected within organizations. Thus, we need to explore more nuanced auxiliary information that can provide improved predictive power and insight into the behavioral factors involved in planning and executing a cyber attack. Evidence suggests that public discourse in online sources, such as social media, is strongly correlated with the occurrence of real-world behavior; we believe this same premise can provide predictive indicators of cyber attacks. For example, extreme negative sentiments towards an organization may indicate a higher probability that it will be the target of a cyber attack. In this paper, we propose to use sentiment in social media as a sensor to better understand, detect, and predict cyber attacks. We develop an effective unsupervised sentiment predictor model utilizing emotional signals, such as emoticons or punctuation, common in social media communications, and a method for using this model as part of a logistic regression predictor to correlate changes in sentiment to the probability of an attack. Experiments on real-world social media data around well-known hacktivist attacks demonstrate the efficacy of the proposed sentiment model for cyber attack understanding and prediction.

1 Charles River Analytics
2 Arizona State University

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