An Artificial Intelligence-Based Improved Grasshopper Optimization Algorithm for Attack and Anomaly Detection in Smart Societies

Abstract

Due to the advanced growth in the development of artificial intelligence (AI) and the Internet of Things (IoT) environment in smart societies, the identification of attacks and anomalies in the IoT environment has become more challenging. Owing to a raised utilization of IoT models in various fields, threats as well as attacks are increased significantly, which spoils the efficiency of the IoT model. In this chapter, an AI-based improved grasshopper optimization algorithm with tumbling effect (IGO-T) for attack and anomaly detection is presented for IoT sites. The presented IGO-T model will perform effective and rapid attack and anomaly identification in the IoT infrastructure. To improve the outcome of the grasshopper optimization algorithm, the tumbling effect is integrated into it. For evaluation purposes, a freely accessible dataset from Kaggle is employed. The dataset holds a total of 357,952 instances along with 13 attributes. The simulation outcome exhibited maximum performance with the accuracy of 99.6% in the training and testing datasets.