Visible to the public One-Class Classification with Deep Autoencoder Neural Networks for Author Verification in Internet Relay Chat

TitleOne-Class Classification with Deep Autoencoder Neural Networks for Author Verification in Internet Relay Chat
Publication TypeConference Paper
Year of Publication2019
AuthorsShao, Sicong, Tunc, Cihan, Al-Shawi, Amany, Hariri, Salim
Conference Name2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)
Keywordsanonymous messaging, anonymous users, arbitrary topics, Author verification, author verification approach, autoencoder, autonomic IRC monitoring system, Computer crime, Computer hacking, cyber-crime, cybersecurity, deep autoencoder neural networks, Deep Learning, effective author verification, electronic messaging, Internet, internet relay chat, Internet Relay Chat (IRC), IRC messages, IRC users, learning (artificial intelligence), Monitoring, neural nets, one-class classification, pattern classification, pubcrawl, Real-time Systems, recursive deep learning, resilience, Resiliency, Scalability, social networking (online), social networks
AbstractSocial 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.
Citation Keyshao_one-class_2019