Intrusion Detection System for IoT Heterogeneous Perceptual Network

Abstract

With the acceleration of the Internet of things (IoT) construction, the security and energy consumption of IoT will become an import factor restricting the overall development of the IoT. In order to reduce the energy consumption of the IoT heterogeneous perceptual network in the attack-defense process, the placement strategy of the intrusion detection system (IDS) described in this paper is to place the IDS on the cluster head nodes selected by the clustering algorithm called ULEACH, which we have proposed in this paper. By optimizing the calculation of the node threshold, the ULEACH clustering algorithm will comprehensively consider the heterogeneity of the perceptual nodes and take the residual energy, energy consumption rate, and overall performance of the nodes into account. As a result, the strategy improves the utilization of the nodes to enhance the performance of heterogeneous perceptual network and extend the lifetime of the system. Furthermore, the paper proposes a intrusion detection system framework and establishes dynamic intrusion detection model for IoT heterogeneous perceptual network based on game theory, by applying modified particle swarm optimization, the optimal defense strategy that could balance the detection efficiency and energy consumption of the system is obtained. Finally, the experiment results show that proposed strategy not only effectively detects multiple network attacks, but also reduces energy consumption.