Kalis - A System for Knowledge-driven Adaptable Intrusion Detection for the Internet of Things
Daniele Midi, Antonino Rullo, Anand Mudgerikar and Elisa Bertino
Purdue University, University of Calabria, Purdue University, Purdue University

In this paper, we introduce Kalis, a self-adapting, knowledge-driven expert Intrusion Detection System able to detect attacks in real time across a wide range of IoT systems. Kalis does not require changes to existing IoT software, can monitor a wide variety of protocols, has no performance impact on applications on IoT devices, and enables collaborative security scenarios. Kalis is the first comprehensive approach to intrusion detection for IoT that does not target individual protocols or applications, and adapts the detection strategy to the specific network features. Extensive evaluation shows that Kalis is effective and efficient in detecting attacks to IoT systems.