Celosia: An Immune-Inspired Anomaly Detection Framework for IoT Devices

Abstract

IoT devices are becoming ubiquitous because of the advent of smart cities and vulnerable to a large number of powerful and sophisticated attacks that can potentially paralyze whole cities. There is a need to develop anomaly detection systems that can work on the same principles as the immune system to continuously learn to detect attacks that are not yet discovered. We present a dynamic framework, Celosia, that is inspired by the immune system offering good accuracy and high performance with minimal human intervention. Celosia employs a continuous learning process to detect abnormal behaviors that are yet to be discovered. It also provides a mechanism to manually define normal and anomalous entities to minimize errors. Celosia provides a layered defence and employs several agents performing their dedicated tasks. Experimental results demonstrate the power and capabilities of this framework, making it an ideal candidate for IoT devices.