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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Economic Worth-Aware Word Embeddings
Knowing the perceived economic value of words is often desirable for applications such as product naming and pricing. However, there is a lack of understanding on the
underlying economic worths of words, even though we have seen some breakthrough on learning the semantics of words. In this work, we bridge this gap by proposing a joint-task
neural network model, Word Worth Model (WWM), to learn word embedding that captures the underlying economic worths. Through the design of WWM, we incorporate contextual factors, e.g., product’s brand name and restaurant’s city, that may affect the aggregated monetary value of a textual item. Via a comprehensive evaluation, we show that, compared with other baselines, WWM accurately predicts missing words when given target words. We also show that the learned embeddings of both words and contextual factors reflect well the underlying economic worths through various visualization analyses.
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- Award ID(s):
- 1717084
- NSF-PAR ID:
- 10303742
- Date Published:
- Journal Name:
- Proceedings of the 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA 2020)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DSAA49011.2020.00048
