A Security Mechanism for Detecting Intrusions in Internet of Things Using Selected Features Based on MI-BGSA

Internet of things (IoT) is a novel emerging approach in computer networks wherein all heterogeneous objects around us, which usually are resource-constrained objects, can connect to each other and also the Internet by using a broad range of technologies. IoT is a hybrid network which includes the Internet and also wireless sensor networks (WSNs) as the main components of IoT; so, implementing security mechanisms in IoT seems necessary. This paper introduces a novel intrusion detection architecture model for IoT that provides the possibility of distributed detection. The proposed hybrid model uses anomaly and misuse intrusion detection agents based on the supervised and unsupervised optimum-path forest models for providing the ability to detect internal and externals attacks, simultaneously. The number of input features to the proposed classifier is reduced by a hybrid feature selection algorithm, as well. The experimental results of simulated scenarios show the superior performance of proposed security mechanism in multi-faceted detection.