Abstract
The need for more research scrutinizing online hacker communities is a common suggestion in recent years. However, researchers and practitioners face many challenges when attempting to do so. In particular, they may encounter hacking-specific terms, concepts, tools, and other items that are unfamiliar and may be challenging to understand. For these reasons, we are motivated to develop an automated method for developing understanding of hacker language. We utilize the latest advancements in recurrent neural network language models (RNNLMs) to develop an unsupervised machine learning technique for learning hacker language. The selected RNNLM produces state-of-the-art word embeddings that are useful for understanding the relations between different hacker terms and concepts. We evaluate our work by testing the RNNLMs ability to learn relevant relations between known hacker terms. Results suggest that the latest work in RNNLMs can aid in modeling hacker language, providing promising direction for future research.
Original language | English (US) |
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Title of host publication | 2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 79-84 |
Number of pages | 6 |
ISBN (Electronic) | 9781479998883 |
DOIs | |
State | Published - Jul 23 2015 |
Event | 13th IEEE International Conference on Intelligence and Security Informatics, ISI 2015 - Baltimore, United States Duration: May 27 2015 → May 29 2015 |
Other
Other | 13th IEEE International Conference on Intelligence and Security Informatics, ISI 2015 |
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Country/Territory | United States |
City | Baltimore |
Period | 5/27/15 → 5/29/15 |
Keywords
- Cybersecurity
- Hacker community
- Language model
- Recurrent neural network
ASJC Scopus subject areas
- Artificial Intelligence
- Law
- Computer Networks and Communications
- Safety, Risk, Reliability and Quality