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

Abstract

<p>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 agents 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 that the number of reports sent by IoT controller were decreased up to 80% while preserving the security aspect.</p>

An extreme gradient boost based classification and regression tree for network intrusion detection in IoT

“…By understanding the evolving strategies of cyber threats and developing advanced detection mechanisms, it can safeguard critical information and infrastructure. This research will empower organizations to proactively counter cyberattacks, ensuring data confidentiality, integrity, and availability, in an interconnected digital world [19], [20]. The objectives of this study are to explore the limitations of existing techniques in detecting attacks in network systems, specifically those reliant on periodic models and extensive training data by using classification methods.…”