Hacker forums provide malicious actors with a large database of tutorials, goods, and assets to leverage for cyber-attacks. Careful research of these forums can provide tremendous benefit to the cybersecurity community through trend identification and exploit categorization. This study aims to provide a novel static word embedding, Hack2Vec, to improve performance on hacker forum classification tasks. Our proposed Hack2Vec model distills contextual representations from the seminal pre-trained language model BERT to a continuous bag-of-words model to create a highly targeted hacker forum static word embedding. The results of our experimental design indicate that Hack2Vec improves performance over prominent embeddings in accuracy, precision, recall, and F1-score for a benchmark hacker forum classification task.
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Effect of Attribute Alignment on Action Sequence Variability: Evidence from Electronic Medical Records, In Business Process Management Forum: BPM Forum 2019, Vienna, Austria, September 2019.
- Award ID(s):
- 1734237
- NSF-PAR ID:
- 10184953
- Date Published:
- Journal Name:
- Lecture notes in business information processing
- ISSN:
- 1865-1348
- Page Range / eLocation ID:
- 183-194
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Online underground forums have been widely used by cybercriminals to trade the illicit products, resources and services, which have played a central role in the cybercrim-inal ecosystem. Unfortunately, due to the number of forums, their size, and the expertise required, it's infeasible to perform manual exploration to understand their behavioral processes. In this paper, we propose a novel framework named iDetector to automate the analysis of underground forums for the detection of cybercrime-suspected threads. In iDetector, to detect whether the given threads are cybercrime-suspected threads, we not only analyze the content in the threads, but also utilize the relations among threads, users, replies, and topics. To model this kind of rich semantic relationships (i.e., thread-user, thread-reply, thread-topic, reply-user and reply-topic relations), we introduce a structured heterogeneous information network (HIN) for representation, which is capable to be composed of different types of entities and relations. To capture the complex relationships (e.g., two threads are relevant if they were posted by the same user and discussed the same topic), we use a meta-structure based approach to characterize the semantic relatedness over threads. As different meta-structures depict the relatedness over threads at different views, we then build a classifier using Laplacian scores to aggregate different similarities formulated by different meta-structures to make predictions. To the best of our knowledge, this is the first work to use structural HIN to automate underground forum analysis. Comprehensive experiments on real data collections from underground forums (e.g., Hack Forums) are conducted to validate the effectiveness of our developed system iDetector in cybercrime-suspected thread detection by comparisons with other alternative methods.more » « less
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Ganguly, Debasis ; Gangopadhyay, Surupendu ; Mitra, Mandar ; Majumder, Prasenjit (Ed.)For most queries, the set of relevant documents spans multiple subtopics. Inspired by the neural ranking models and query-specific neural clustering models, we develop Topic-Mono-BERT which performs both tasks jointly. Based on text embeddings of BERT, our model learns a shared embedding that is optimized for both tasks. The clustering hypothesis would suggest that embeddings which place topically similar text in close proximity will also perform better on ranking tasks. Our model is trained with the Wikimarks approach to obtain training signals for relevance and subtopics on the same queries. Our task is to identify overview passages that can be used to construct a succinct answer to the query. Our empirical evaluation on two publicly available passage retrieval datasets suggests that including the clustering supervision in the ranking model leads to about 16% improvement in identifying text passages that summarize different subtopics within a query.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-26643-1_11
