Pelican Optimization Algorithm with Federated Learning Driven Attack Detection model in Internet of Things environment

Abstract

The Internet of Things (IoT) is comprised of millions of physical devices interconnected with the Internet through network that performs a task independently with less human interference. Despite the benefits of IoT, it is vulnerable to malicious attacks. Therefore, machine learning (ML) inspired decision-making is hardly preferred due to drawbacks (the requirement that every training dataset is stored on the central database), computation cost related to the training of massive amounts of information on the unified server, and security issues related to transferring attained statistics from IoT sensor to the server. The Federated Learning (FL) algorithm is the adaptable and most prominent way to resolve the shortcoming of ML-based techniques. The study proposes a Pelican Optimization Algorithm with Federated Learning Driven Attack Detection and classification (POAFL-DDC) technique in the IoT. The POAFL-DDC technique operates on decentralized on-device data for attack detection in the IoT. By substituting update weight with the central FL server, the data is placed on the local IoT device whereas federating training cycles on the DL model. For attack detection process, deep belief network (DBN) model is used in this work. In this study, the POA is utilized to optimize the DBN hyperparameters. The experimental validation of the POAFL-DDC algorithm is tested on TON_IOT dataset and the results are examined in terms of different measures. The experimental outcomes demonstrated that the POAFL-DDC technique reaches superior results over other models.