Collaborative intrusion detection networks with multi-hop clustering for internet of things

Abstract

Internet of things (IoT) is an emerging topic in so many aspects nowadays. The integration between devices and human itself is currently in large scale development. With the continuous applications of the IoT, the hidden problems such as security threats become one of the key considerations. Furthermore, limited power and computational capability of the devices in the system make it more challenging. Therefore, the needs of reliable and effective security system throughout the networks are highly needed. This research proposed a collaborative system based on JADE that consists of 3 types of agent, which are IoT server, controller, and node. Every agent will collaborate each other in terms of exchanging the intrusion detection results. The collaboration between the agents will provide more efficient and good performance. Four classification algorithms were used to model IDS functions. Then, the performance evaluation was done on the system with several parameters such as cost loss expectation, energy consumption, and metric of IDS efficiency. The result shows the number of reports sent by IoT controller were decreased up to 80% while preserving the security aspect.