REST: A Thread Embedding Approach for Identifying and Classifying User-Specified Information in Security Forums

Authors

  • Joobin Gharibshah University of California Riverside
  • Evangelos E. Papalexakis University of California Riverside
  • Michalis Faloutsos University of California Riverside

Abstract

How can we extract useful information from a security forum? We focus on identifying threads of interest to a security professional: (a) alerts of worrisome events, such as attacks, (b) offering of malicious services and products, (c) hacking information to perform malicious acts, and (d) useful security-related experiences. The analysis of security forums is in its infancy despite several promising recent works. Novel approaches are needed to address the challenges in this domain: (a) the difficulty in specifying the “topics” of interest efficiently, and (b) the unstructured and informal nature of the text. We propose, REST, a systematic methodology to: (a) identify threads of interest based on a, possibly incomplete, bag of words, and (b) classify them into one of the four classes above. The key novelty of the work is a multi-step weighted embedding approach: we project words, threads and classes in appropriate embedding spaces and establish relevance and similarity there. We evaluate our method with real data from three security forums with a total of 164k posts and 21K threads. First, REST robustness to initial keyword selection can extend the user-provided keyword set and thus, it can recover from missing keywords. Second, REST categorizes the threads into the classes of interest with superior accuracy compared to five other methods: REST exhibits an accuracy between 63.3-76.9%. We see our approach as a first step for harnessing the wealth of information of online forums in a user-friendly way, since the user can loosely specify her keywords of interest.

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Published

2020-05-26

How to Cite

Gharibshah, J., Papalexakis, E. E., & Faloutsos, M. (2020). REST: A Thread Embedding Approach for Identifying and Classifying User-Specified Information in Security Forums. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 217-228. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/7293