Abstract
Internet Relay-Chat (IRC) is a real-time communication protocol that allows broadcasting and direct messages in the form of text. Hence, IRC has been widely used especially by hacker communities to communicate and plan malicious activities. Even though widely used for malicious intent, little research has been done on the analysis of the social network among hacker communities in IRC. Hence, it is crucial to analyze IRC communities and their connection. In this paper, we classified IRC messages based on their intent and created their communication graphs to compute metadata on the relation between hackers. For this purpose, we apply autonomic computing for IRC monitoring and data collection, perform deep learning to classify IRC messages into different threat levels, and then apply the quasi-clique model to analyze hacker social networks, and identify the hidden relations between them.
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Acknowledgements
This work is partly supported by the Air Force Office of Scientific Research (AFOSR) Dynamic Data-Driven Application Systems (DDDAS) award number FA9550-18-1-0427, National Science Foundation (NSF) research projects NSF-1624668 and SES-1314631, and Thomson Reuters in the framework of the Partner University Fund (PUF) project (PUF is a program of the French Embassy in the United States and the FACE Foundation and is supported by American donors and the French government).
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Bernard, J., Shao, S., Tunc, C., Kheddouci, H., Hariri, S. (2019). Quasi-cliques Analysis for IRC Channel Thread Detection. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_47
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DOI: https://doi.org/10.1007/978-3-030-05411-3_47
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