Autoencoder Ensemble Method for Botnets Detection on IOT Devices.

Like anything else on the internet, IoT devices are very susceptible to cyber-attacks that could take out the device or install spyware. In this paper, we propose an anomaly detection solution driven by an autoencoder ensemble to detect botnets on IOT devices. In particular, the ensemble size is determined by hierarchical clustering of the features in the packet header. Moreover, one does not require an additional neural network to combine the decisions. The proposed approach is a more efficient solution for IOT problem setting and hence, overcomes the issue of lacking computational resources and memory on IOT devices, as well as run-time performance problems. Empirical results on two datasets, one from the 2016 Mirai botnet attacks on IoT devices and the other from Gafgyt malware attacks on various IOT devices, show the competitiveness and feasibility of our proposed solution.