Next-generation intrusion detection in cyber-physical systems using an ensemble of quantum-inspired and deep neural models

Abstract

Cyber-physical systems (CPSs) could cause actuation and safety risks. Intrusion detection is essential for preserving the system's integrity due to growing security issues. Nowadays, deep learning (DL) schemes have been deployed to enhance the detection of cyber-attacks, yet these models are prone to overfitting, which reduces detection performance. Hence, this research proposes a novel deep learning-based Intrusion Detection System (IDS) for CPS to address these limitations. The proposed methodology consists of four key stages, including preprocessing, feature extraction, feature selection, and intrusion detection. Data preprocessing is performed via cleansing, followed by the extraction of statistical [mean, median, and standard deviation (SD)], entropy-based, improved correlation, improved mutual information (MI), flow-based, and Improved Information Gain (IIG) features, which are derived to obtain the important features. The Archimedes Algorithm with Team Work Principle (AA_TWP), integrating the Archimedes Optimization Algorithm (AOA) and the Teamwork Optimization Algorithm (TOA), with modifications to the exploration phase, is employed to efficiently select the most relevant features. The selected features, along with preprocessed data, are fed into an ensemble of Deep Belief Networks (DBNs), Quantum Deep Neural Networks (QDNNs), and optimized Bidirectional Long Short-Term Memory (Bi-LSTM), with Bi-LSTM weights further tuned by AA_TWP. The ensemble outputs are averaged to produce the final intrusion decision. Experimental results demonstrate 91.52% accuracy and 91.76% Matthews Correlation coefficient (MCC), highlighting the effectiveness of the proposed approach, which outperforms existing techniques.