Enhanced security for IoT networks: a hybrid optimized learning model for intrusion classification

The Internet of Things (IoT) features multiple device connectivity and breaks the conventional network connectivity limitations like limited wireless range, scalability specific communication protocol dependency, etc. Multiple devices can be connected in an IoT network without significant infrastructure changes and the devices can communicate with each other through variety of protocols, which could be more beneficial in many organizations, consumers, and governments. However, the rapid development of IoT technology requires a secure network as it must access different devices and communication methods. This diversity and heterogeneity make network intrusions more convenient for intruders. IoT network complexity and security flaws increase when a large volume of data is transferred through a network. Intrusion detection systems (IDS) are used to monitor the network behavior for detecting unusual behaviors or intrusions. Numerous machine learning models are used in IDS for classifying network traffic. However, these methods lag in detection performances due to limited feature handling abilities. Thus, in selecting optimal features that correctly indicate the intrusions in the network, optimization models are used in IDSs. However, due to the limited exploration and exploitation ability of conventional optimization algorithms, this research presents a hybrid optimization algorithm using Salp Swarm Optimization and Bee Foraging (SSA-BF) optimization approaches for optimal feature selection. The optimal features are classified using a multiplicative Long Short-Term Memory (MLSTM) network. To check the robustness of the proposed IDS, accuracy, recall, f1-score, and precision metrics are considered for analysis. Simulation results of the proposed IDS exhibited a maximum accuracy of 95.8%, better than conventional Auto Encoder, Convolutional Neural Network, Gaussian mixture model with Generative adversarial Network, Multi-CNN, and DeepNet-based IDSs.