IGWO-SoE: Improved Grey Wolf Optimization Based Stack of Ensemble Learning Algorithm for Anomaly Detection in Internet of Things Edge Computing

With the tremendous growth and popularization of the Internet of Things (IoT), the number of attacks targeting such devices has also increased. Therefore, enhancing the anomaly detection model to maximize detection accuracy and mitigate cyber-attacks in time-critical IoT edge scenarios is essential. Furthermore, there is a lack of vivid, precise, cross-layered, and diverse datasets in IoT for evaluating these anomaly detection models. This paper aims to develop an improved anomaly detection model based on an optimized stacked ensemble learning algorithm at edge computing. Initially, a novel synthetic dataset with multiple cross-layer attacks is generated using the Cooja simulator to train our proposed model. In addition, by introducing an improved grey wolf optimization (IGWO) approach, the parameters of ensemble learning algorithms, such as number of trees, learning rate, and sample rate, are tuned precisely, and the stacking ensemble concept is applied to the optimized ensemble learning algorithms to enhance their prediction capabilities. The experimental results demonstrate that the developed model produces a detection accuracy of 99.44% for our proposed Cooja simulated dataset, which is higher than the contemporary methods. The generalizability of the proposed model is expressed explicitly using four different datasets: NSL KDD, UNSW NB 15, MQTTset, and CICIDS 2017. Finally, we assess the befitting of the proposed model using a chi-square statistical significance test, thereby providing an enriched contribution to the recent works in anomaly detection.