TY - GEN
T1 - One-class classification with deep autoencoder neural networks for author verification in internet relay chat
AU - Shao, Sicong
AU - Tunc, Cihan
AU - Al-Shawi, Amany
AU - Hariri, Salim
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Social networks are highly preferred to express opinions, share information, and communicate with others on arbitrary topics. However, the downside is that many cybercriminals are leveraging social networks for cyber-crime. Internet Relay Chat (IRC) is the important social networks which can grant the anonymity to users by allowing them to connect channels without sign-up process. Therefore, IRC has been the playground of hackers and anonymous users for various operations such as hacking, cracking, and carding. Hence, it is urgent to study effective methods which can identify the authors behind the IRC messages. In this paper, we design an autonomic IRC monitoring system, performing recursive deep learning for classifying threat levels of messages and develop a novel author verification approach with one-class classification with deep autoencoder neural networks. The experimental results show that our approach can successfully perform effective author verification for IRC users.
AB - Social networks are highly preferred to express opinions, share information, and communicate with others on arbitrary topics. However, the downside is that many cybercriminals are leveraging social networks for cyber-crime. Internet Relay Chat (IRC) is the important social networks which can grant the anonymity to users by allowing them to connect channels without sign-up process. Therefore, IRC has been the playground of hackers and anonymous users for various operations such as hacking, cracking, and carding. Hence, it is urgent to study effective methods which can identify the authors behind the IRC messages. In this paper, we design an autonomic IRC monitoring system, performing recursive deep learning for classifying threat levels of messages and develop a novel author verification approach with one-class classification with deep autoencoder neural networks. The experimental results show that our approach can successfully perform effective author verification for IRC users.
KW - Author verification
KW - Autoencoder
KW - Cybersecurity
KW - Deep learning
KW - Internet Relay Chat (IRC)
UR - http://www.scopus.com/inward/record.url?scp=85082657214&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082657214&partnerID=8YFLogxK
U2 - 10.1109/AICCSA47632.2019.9035309
DO - 10.1109/AICCSA47632.2019.9035309
M3 - Conference contribution
AN - SCOPUS:85082657214
T3 - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
BT - 16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019
PB - IEEE Computer Society
T2 - 16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019
Y2 - 3 November 2019 through 7 November 2019
ER -