IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

Umang Garg, Santosh Kumar () and Aniket Mahanti ()
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Umang Garg: Computer Science and Engineering, Amity University, Gwalior 201301, India
Santosh Kumar: Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
Aniket Mahanti: School of Computer Science, University of Auckland, Auckland 1010, New Zealand

Future Internet, 2024, vol. 16, issue 6, 1-13

Abstract: The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

Keywords: IoT botnet; botnet mitigation techniques; stacking ensemble classifier (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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