Visible to the public GANIBOT: A Network Flow Based Semi Supervised Generative Adversarial Networks Model for IoT Botnets Detection

TitleGANIBOT: A Network Flow Based Semi Supervised Generative Adversarial Networks Model for IoT Botnets Detection
Publication TypeConference Paper
Year of Publication2022
AuthorsSaurabh, Kumar, Singh, Ayush, Singh, Uphar, Vyas, O.P., Khondoker, Rahamatullah
Conference Name2022 IEEE International Conference on Omni-layer Intelligent Systems (COINS)
Date Publishedaug
KeywordsBotnet, botnets, botnets security, composability, compositionality, DDoS Attack, generative adversarial networks, Internet of Things, Intrusion detection, IoT security, Medical services, Metrics, Protocols, pubcrawl, resilience, Resiliency, Semi supervised GAN, telecommunication traffic
AbstractThe spread of Internet of Things (IoT) devices in our homes, healthcare, industries etc. are more easily infiltrated than desktop computers have resulted in a surge in botnet attacks based on IoT devices, which may jeopardize the IoT security. Hence, there is a need to detect these attacks and mitigate the damage. Existing systems rely on supervised learning-based intrusion detection methods, which require a large labelled data set to achieve high accuracy. Botnets are onerous to detect because of stealthy command & control protocols and large amount of network traffic and hence obtaining a large labelled data set is also difficult. Due to unlabeled Network traffic, the supervised classification techniques may not be used directly to sort out the botnet that is responsible for the attack. To overcome this limitation, a semi-supervised Deep Learning (DL) approach is proposed which uses Semi-supervised GAN (SGAN) for IoT botnet detection on N-BaIoT dataset which contains "Bashlite" and "Mirai" attacks along with their sub attacks. The results have been compared with the state-of-the-art supervised solutions and found efficient in terms of better accuracy which is 99.89% in binary classification and 59% in multi classification on larger dataset, faster and reliable model for IoT Botnet detection.
DOI10.1109/COINS54846.2022.9854947
Citation Keysaurabh_ganibot_2022