Abstract

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.

Original languageEnglish (US)
Title of host publicationSocial, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings
EditorsHalil Bisgin, Robert Thomson, Ayaz Hyder, Christopher Dancy
PublisherSpringer Verlag
Pages377-388
Number of pages12
ISBN (Print)9783319933719
DOIs
StatePublished - Jan 1 2018
Event11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018 - Washington, United States
Duration: Jul 10 2018Jul 13 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10899 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction conference and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2018
CountryUnited States
CityWashington
Period7/10/187/13/18

Keywords

  • Cyber attack
  • Sentiment analysis
  • Social media

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Shu, K., Sliva, A., Sampson, J., & Liu, H. (2018). Understanding cyber attack behaviors with sentiment information on social media. In H. Bisgin, R. Thomson, A. Hyder, & C. Dancy (Eds.), Social, Cultural, and Behavioral Modeling - 11th International Conference, SBP-BRiMS 2018, Proceedings (pp. 377-388). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10899 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93372-6_41