TY - GEN
T1 - Autonomic Author Identification in Internet Relay Chat (IRC)
AU - Shao, Sicong
AU - Tunc, Cihan
AU - Al-Shawi, Amany
AU - Hariri, Salim
N1 - Funding Information:
ACKNOWLEDGMENT 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:
© 2018 IEEE.
PY - 2019/1/14
Y1 - 2019/1/14
N2 - With the advances in Internet technologies and services, the social media has been gaining excessive popularity, especially because these technologies provide anonymity where they use nicknames to post their messages. Unfortunately, the anonymity feature has been exploited by the cyber-criminals to hide their identities and their operations. Hence, there is a growing interest in cybersecurity research domain to identify the authors of malicious messages and activities. Internet Relay Chat (IRC) channels are widely used to exchange messages and information among malicious users involved in cybercrimes. In this paper, we present an autonomic author identification technique based on personality profile and analysis of IRC messages. We first monitor the IRC channels using our autonomic bots and then create a personality profile for each targeted author. We demonstrate that personality analysis for author detection/identification is an efficient approach and has high detection rates.
AB - With the advances in Internet technologies and services, the social media has been gaining excessive popularity, especially because these technologies provide anonymity where they use nicknames to post their messages. Unfortunately, the anonymity feature has been exploited by the cyber-criminals to hide their identities and their operations. Hence, there is a growing interest in cybersecurity research domain to identify the authors of malicious messages and activities. Internet Relay Chat (IRC) channels are widely used to exchange messages and information among malicious users involved in cybercrimes. In this paper, we present an autonomic author identification technique based on personality profile and analysis of IRC messages. We first monitor the IRC channels using our autonomic bots and then create a personality profile for each targeted author. We demonstrate that personality analysis for author detection/identification is an efficient approach and has high detection rates.
KW - Author identification
KW - Internet Relay Chat (IRC)
KW - Watson AI platform
KW - cybersecurity
KW - machine learning
KW - personality insights
UR - http://www.scopus.com/inward/record.url?scp=85061901586&partnerID=8YFLogxK
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U2 - 10.1109/AICCSA.2018.8612780
DO - 10.1109/AICCSA.2018.8612780
M3 - Conference contribution
AN - SCOPUS:85061901586
T3 - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
BT - 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications, AICCSA 2018
PB - IEEE Computer Society
T2 - 15th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2018
Y2 - 28 October 2018 through 1 November 2018
ER -