A Deep Fusion Model For Automated Industrial Iot Cyber Attack Detection And Mitigation

Abstract

Industrial Internet of Things (IIoT) is a new field of study that connects digital devices and services to physical systems. The IIoT has been utilized to create massive amounts of data from various sensors, and it has run into several problems. The IIoT has been subjected to a variety of hacks, putting its ability to provide enterprises with flawless operations in jeopardy. Businesses suffer financial and reputational losses as a result of such threats, as well as the theft of critical data. As a result, numerous Network Intrusion Detection Systems (NIDSs) have been created to combat and safeguard IIoT systems, but gathering data that can be utilized in the construction of an intelligent NIDS is a tough operation; consequently, identifying current and new assaults poses major issues. In this research work, a novel IIOT attack detection framework and mitigation model is designed by following four major phases “(a) pre-processing, (b) feature extraction, (c) feature selection and (d) attack detection”. Initially, the collected raw data (input) is subjected to pre-processing phase, wherein the data cleaning and data standardization operations take place. Subsequently, the features like “higher-order statistical features (Skewness, Kurtosis, Variance and Moments), technical indicator based features, mutual information, Improved Principal Component Analysis (IPCA)” based features are extracted from the pre-processed data. Further, from the extracted features, the most optimal features are selected using a new hybrid optimization model referred as Hunger Customized Individual Activity Model (HCIA) that hybrids the concepts of standard (Teamwork Optimization Algorithm (TOA) and Hunger Games Search (HGS)). The attack detection is carried out using the projected deep fusion model framework that encapsulates the Bi-GRU and Quantum Deep Neural Network (QDNN), respectively. The Bi-GRU and QDNN in the deep fusion model framework is trained with the optimal features selected using a new hybrid optimization model. The outcome acquired from Bi-GRU and QDNN is combined, and it will be the final detected outcome that portrays the presence/ absence of attacks in IIoT network. When an attack is being identified, the mitigation of such attack takes place via the Improved BIAT Framework. Further, the projected model is evaluated over the existing models to show its supremacy in the attack detection and mitigation process.