A new intrusion detection method for cyber–physical system in emerging industrial IoT

Mittal, Himanshu; Tripathi, Ashish; Pandey, Avinash Chandra; Alshehri, Mohammad Dahman; Saraswat, Mukesh; Pal, Raju

doi:10.1016/j.comcom.2022.04.004

Cited by 13 publications

(1 citation statement)

References 35 publications

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“…Mittal et al [ 16 ] introduced a new technique for the IDS. The presented technique applied a new variant of the gravitational search technique to attain the optimum cluster.…”

Section: Related Workmentioning

confidence: 99%

Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment

Almuqren

Al-Mutiri

Maashi

et al. 2023

Sensors

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A Cyber-Physical System (CPS) is a network of cyber and physical elements that interact with each other. In recent years, there has been a drastic increase in the utilization of CPSs, which makes their security a challenging problem to address. Intrusion Detection Systems (IDSs) have been used for the detection of intrusions in networks. Recent advancements in the fields of Deep Learning (DL) and Artificial Intelligence (AI) have allowed the development of robust IDS models for the CPS environment. On the other hand, metaheuristic algorithms are used as feature selection models to mitigate the curse of dimensionality. In this background, the current study presents a Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-based Intrusion Detection (SCAVO-EAEID) technique to provide cybersecurity in CPS environments. The proposed SCAVO-EAEID algorithm focuses mainly on the identification of intrusions in the CPS platform via Feature Selection (FS) and DL modeling. At the primary level, the SCAVO-EAEID technique employs Z-score normalization as a preprocessing step. In addition, the SCAVO-based Feature Selection (SCAVO-FS) method is derived to elect the optimal feature subsets. An ensemble Deep-Learning-based Long Short-Term Memory–Auto Encoder (LSTM-AE) model is employed for the IDS. Finally, the Root Means Square Propagation (RMSProp) optimizer is used for hyperparameter tuning of the LSTM-AE technique. To demonstrate the remarkable performance of the proposed SCAVO-EAEID technique, the authors used benchmark datasets. The experimental outcomes confirmed the significant performance of the proposed SCAVO-EAEID technique over other approaches with a maximum accuracy of 99.20%.

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“…Mittal et al [ 16 ] introduced a new technique for the IDS. The presented technique applied a new variant of the gravitational search technique to attain the optimum cluster.…”

Section: Related Workmentioning

confidence: 99%