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Early Detection of Mirai-Like IoT Bots in Large-Scale Networks through Sub-sampled Packet Traffic Analysis

  • Ayush KumarEmail author
  • Teng Joon Lim
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 70)

Abstract

The widespread adoption of Internet of Things has led to many security issues. Recently, there have been malware attacks on IoT devices, the most prominent one being that of Mirai. IoT devices such as IP cameras, DVRs and routers were compromised by the Mirai malware and later large-scale DDoS attacks were propagated using those infected devices (bots) in October 2016. In this research, we develop a network-based algorithm which can be used to detect IoT bots infected by Mirai or similar malware in large-scale networks (e.g. ISP network). The algorithm particularly targets bots scanning the network for vulnerable devices since the typical scanning phase for botnets lasts for months and the bots can be detected much before they are involved in an actual attack. We analyze the unique signatures of the Mirai malware to identify its presence in an IoT device. Further, to optimize the usage of computational resources, we use a two-dimensional (2D) packet sampling approach, wherein we sample the packets transmitted by IoT devices both across time and across the devices. Leveraging the Mirai signatures identified and the 2D packet sampling approach, a bot detection algorithm is proposed. We use testbed measurements and simulations to study the relationship between bot detection delays and the sampling frequencies for device packets. Subsequently, we derive insights from the obtained results and use them to design our proposed bot detection algorithm. Finally, we discuss the deployment of our bot detection algorithm and the countermeasures which can be taken post detection.

Keywords

Internet of Things IoT Malware Mirai Botnet Bot Detection 

Notes

Acknowledgements

The authors would like to thank Dr. Liang Zhenkai (SoC, NUS) for helping us with some of the initial ideas used in this paper and Dr. Min Suk Kang (SoC, NUS) for providing comments on our manuscript. We would also like to appreciate the National Cybersecurity R&D Lab, Singapore for allowing us to use their testbed to collect important data which has been used in our work. This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Corporate Laboratory@University Scheme, National University of Singapore, and Singapore Telecommunications Ltd.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National University of SingaporeSingaporeSingapore

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