TY - GEN
T1 - Quasi-cliques Analysis for IRC Channel Thread Detection
AU - Bernard, Jocelyn
AU - Shao, Sicong
AU - Tunc, Cihan
AU - Kheddouci, Hamamache
AU - Hariri, Salim
N1 - Funding Information:
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).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85059095831&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-05411-3_47
DO - 10.1007/978-3-030-05411-3_47
M3 - Conference contribution
AN - SCOPUS:85059095831
SN - 9783030054106
T3 - Studies in Computational Intelligence
SP - 578
EP - 589
BT - Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018
A2 - Lambiotte, Renaud
A2 - Rocha, Luis M.
A2 - Lió, Pietro
A2 - Cherifi, Hocine
A2 - Aiello, Luca Maria
A2 - Cherifi, Chantal
PB - Springer-Verlag
T2 - 7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018
Y2 - 11 December 2018 through 13 December 2018
ER -