Developing understanding of hacker language through the use of lexical semantics

Victor Benjamin; Hsinchun Chen

doi:10.1109/ISI.2015.7165943

Developing understanding of hacker language through the use of lexical semantics

Victor Benjamin, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Scopus citations

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)
Title of host publication	2015 IEEE International Conference on Intelligence and Security Informatics
Subtitle of host publication	Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015
Editors	Lina Zhou, G. Alan Wang, Wenji Mao, Lisa Kaati
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	79-84
Number of pages	6
ISBN (Electronic)	9781479998883
DOIs	https://doi.org/10.1109/ISI.2015.7165943
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

Publication series

Name	2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015

Other

Other	13th IEEE International Conference on Intelligence and Security Informatics, ISI 2015
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

Access to Document

10.1109/ISI.2015.7165943

Cite this

Benjamin, V., & Chen, H. (2015). Developing understanding of hacker language through the use of lexical semantics. In L. Zhou, G. A. Wang, W. Mao, & L. Kaati (Eds.), 2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015 (pp. 79-84). Article 7165943 (2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2015.7165943

Developing understanding of hacker language through the use of lexical semantics. / Benjamin, Victor; Chen, Hsinchun.
2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015. ed. / Lina Zhou; G. Alan Wang; Wenji Mao; Lisa Kaati. Institute of Electrical and Electronics Engineers Inc., 2015. p. 79-84 7165943 (2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Benjamin, V & Chen, H 2015, Developing understanding of hacker language through the use of lexical semantics. in L Zhou, GA Wang, W Mao & L Kaati (eds), 2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015., 7165943, 2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015, Institute of Electrical and Electronics Engineers Inc., pp. 79-84, 13th IEEE International Conference on Intelligence and Security Informatics, ISI 2015, Baltimore, United States, 5/27/15. https://doi.org/10.1109/ISI.2015.7165943

Benjamin V, Chen H. Developing understanding of hacker language through the use of lexical semantics. In Zhou L, Wang GA, Mao W, Kaati L, editors, 2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 79-84. 7165943. (2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015). doi: 10.1109/ISI.2015.7165943

Benjamin, Victor ; Chen, Hsinchun. / Developing understanding of hacker language through the use of lexical semantics. 2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015. editor / Lina Zhou ; G. Alan Wang ; Wenji Mao ; Lisa Kaati. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 79-84 (2015 IEEE International Conference on Intelligence and Security Informatics: Securing the World through an Alignment of Technology, Intelligence, Humans and Organizations, ISI 2015).

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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.",

keywords = "Cybersecurity, Hacker community, Language model, Recurrent neural network",

author = "Victor Benjamin and Hsinchun Chen",

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AB - 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.

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Developing understanding of hacker language through the use of lexical semantics

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