Visible to the public AI-Powered Honeypots for Enhanced IoT Botnet Detection

TitleAI-Powered Honeypots for Enhanced IoT Botnet Detection
Publication TypeConference Paper
Year of Publication2020
AuthorsMemos, V. A., Psannis, K. E.
Conference Name2020 3rd World Symposium on Communication Engineering (WSCE)
Date PublishedOct. 2020
ISBN Number978-1-7281-8564-4
KeywordsAI-powered honeypots, Botnet, botnets, cloud computing, composability, compromised devices, Computer crime, Computer hacking, computer network security, connected devices, conventional security architectures, DDoS, denial-of-service attack, enhanced IoT botnet detection rate, hackers, honeypots, Internet of Things, invasive software, IoT, IoT network, learning (artificial intelligence), malicious activities, malicious software, Malware, malware spread, Metrics, MITM, novel hybrid Artificial Intelligence-powered honeynet, potential botnet existence, pubcrawl, resilience, Resiliency, revolutionary expandable network, security, security gaps, Servers, upcoming security mechanism

Internet of Things (IoT) is a revolutionary expandable network which has brought many advantages, improving the Quality of Life (QoL) of individuals. However, IoT carries dangers, due to the fact that hackers have the ability to find security gaps in users' IoT devices, which are not still secure enough and hence, intrude into them for malicious activities. As a result, they can control many connected devices in an IoT network, turning IoT into Botnet of Things (BoT). In a botnet, hackers can launch several types of attacks, such as the well known attacks of Distributed Denial of Service (DDoS) and Man in the Middle (MitM), and/or spread various types of malicious software (malware) to the compromised devices of the IoT network. In this paper, we propose a novel hybrid Artificial Intelligence (AI)-powered honeynet for enhanced IoT botnet detection rate with the use of Cloud Computing (CC). This upcoming security mechanism makes use of Machine Learning (ML) techniques like the Logistic Regression (LR) in order to predict potential botnet existence. It can also be adopted by other conventional security architectures in order to intercept hackers the creation of large botnets for malicious actions.

Citation Keymemos_ai-powered_2020