Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

Abstract

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

Golden Jackal Optimization with a Deep Learning-Based Cybersecurity Solution in Industrial Internet of Things Systems

“…ML can enable the experiential learning and decision-making method of the different systems by enhancing the expertise and capability without the use of open programming [13]. In IDSs that are made via employing DL approaches, recent and updated databases are selected to execute widespread intrusion detection [14]. DL-based IDSs are generally trained by employing state-of-the-art databases that have been produced for intrusion detection [15].…”

Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment

“…With an increase in the number of network attacks, the quantity of data accessible on networks increases; thus, rapid and highly effective methods are required in order to identify the attacks. Without a doubt, a huge number of approaches to enhancing network security exist [10]. Privacy and security are significant IoT challenges to focus on and investigate further.…”