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The widespread adoption of Internet of Things has led to many securityissues. Recently, there have been malware attacks on IoT devices, the mostprominent one being that of Mirai. IoT devices such as IP cameras, DVRs androuters were compromised by the Mirai malware and later large-scale DDoSattacks were propagated using those infected devices (bots) in October 2016. Inthis research, we develop a network-based algorithm which can be used to detectIoT bots infected by Mirai or similar malware in large-scale networks (e.g. ISPnetwork). The algorithm particularly targets bots scanning the network forvulnerable devices since the typical scanning phase for botnets lasts formonths and the bots can be detected much before they are involved in an actualattack. We analyze the unique signatures of the Mirai malware to identify itspresence in an IoT device. The prospective deployment of our bot detectionsolution is discussed next along with the countermeasures which can be takenpost detection. Further, to optimize the usage of computational resources, weuse a two-dimensional (2D) packet sampling approach, wherein we sample thepackets 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. Subsequently, we use testbedmeasurements and simulations to study the relationship between bot detectiondelays and the sampling frequencies for device packets. Finally, we deriveinsights from the obtained results and use them to design our proposed botdetection algorithm.

Early Detection Of Mirai-Like IoT Bots In Large-Scale Networks Through Sub-Sampled Packet Traffic Analysis