Identifying Malicious Nodes in Multihop IoT Networks Using Diversity and Unsupervised Learning

The increased connectivity introduced in Internet of Things (IoT) applications makes such systems vulnerable to serious security threats. In this paper, we consider one of the most challenging threats in IoT networks, where devices manipulate (maliciously or unintentionally) the data transmitted in information packets as they are being forwarded from the source to the destination. We propose unsupervised learning that exploits network diversity...