Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment

Alohali, Manal Abdullah; Al‐Wesabi, Fahd N.; Hilal, Anwer Mustafa; Goel, Shalini; Gupta, Deepak; Khanna, Ashish

doi:10.1007/s11571-022-09780-8

Cited by 54 publications

(26 citation statements)

References 27 publications

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“…Alohali et al [11] introduced an artificial intelligence-enabled multimodal fusion-based intrusion detection system (AIMMF-IDS) for CCPS (cognitive cyber-physical system) in the environment industry 4.0. The CCPS has various limitations and security issues due to their challenging design.…”

Section: Related Workmentioning

confidence: 99%

KHO-XAI: Krill herd optimization and Explainable Artificial Intelligence framework for Network Intrusion Detection Systems in Industry 4.0

Sivamohan

2022

Preprint

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Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a few. However, they are more susceptible to a broad range of cyber threats because of limited resources and heterogeneous nature. Such risks cause financial and reputational damages for businesses, well as the theft of sensitive information. The higher level of diversity in industrial network prevents the attackers from such attacks. Therefore, to efficiently detect the intrusions, a novel intrusion detection system known as Bidirectional Long Short-Term Memory based Explainable Artificial Intelligence framework (BiLSTM-XAI) is developed. Initially, the preprocessing task using normalization and standardization is performed to enhance the data quality for detecting network intrusions. Subsequently, the significant features are selected from the databases using the Krill herd optimization (KHO) algorithm. The proposed BiLSTM-XAI approach provides better security and privacy inside the industry networking system by detecting intrusions very precisely. In this, we utilized SHAP and LIME explainable AI algorithms to further improve the prediction accuracy. The experimental setup is made by MATLAB 2016 software using Honeypot and NSL-KDD datasets as input. The analysis result reveals that the proposed method achieves superior performance in detecting intrusions with a classification accuracy of 98.2%.

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Section: Related Workmentioning

confidence: 99%

KHO-XAI: Krill herd optimization and Explainable Artificial Intelligence framework for Network Intrusion Detection Systems in Industry 4.0

Sivamohan

2022

Preprint

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“…The experiment results inferred that the presented technique can enhance the scalability and detection efficiency. Cheng et al [18][19][20] presented a temporal convolution network with global attention model to develop an in-vehicle network IDS named TCAN-IDS. The feature extraction method extracts the spatial-temporal details.…”

Section: Related Workmentioning

confidence: 99%

Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

Hamza¹,

Hashim²,

Mohamed³

et al. 2022

Computers, Materials &Amp; Continua

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Internet of Everything (IoE), the recent technological advancement, represents an interconnected network of people, processes, data, and things. In recent times, IoE gained significant attention among entrepreneurs, individuals, and communities owing to its realization of intense values from the connected entities. On the other hand, the massive increase in data generation from IoE applications enables the transmission of big data, from contextaware machines, into useful data. Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems (IDS). In this background, the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System (IMVO-DLIDS) for IoT environment. The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment. The proposed IMVO-DLIDS model follows a three-stage process. At first, data pre-processing is performed to convert the actual data into useful format. In addition, Chaotic Local Search Whale Optimization Algorithm-based Feature Selection (CLSWOA-FS) technique is employed to choose the optimal feature subsets. Finally, MVO algorithm is exploited with Bidirectional Gated Recurrent Unit (BiGRU) model for classification. Here, the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model. The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct 6580 CMC, 2022, vol.73, no.3 measures. An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

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“…Therefore, many applications are created to expand the lifespan of WSNs. Sensor Nodes function on the basis of in-built battery due to which WSNs experience energyconstraint issues [8][9][10]. Therefore, the dissipation of power is controlled to extend the lifespan of the network.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

Asiri¹,

Alotaibi²,

Elkamchouchi³

et al. 2022

Computers, Materials &Amp; Continua

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Wireless Sensor Network (WSN) is a vital element in Internet of Things (IoT) as the former enables the collection of huge quantities of data in energy-constrained environment. WSN offers independent access to the target region and performs data collection in an effective manner. But energy constraints remain a challenging issue in WSN since it operates on in-built battery. The studies conducted earlier recommended that the energy spent on communication process must be considerably reduced to improve the efficiency of WSN. Cluster organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic algorithms. Clustering and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of WSN. In this background, the current study develops an Improved Duck and Traveller Optimization (IDTO)-enabled cluster-based Multi-Hop Routing (IDTOMHR) technique for WSN. Primarily, IDTO algorithm is exploited for the selection of Cluster Head (CH) and construction of clusters. Besides, Artificial Gorilla Troops Optimization (ATGO) technique is also used to derive an optimal set of routes to the destination. Both clustering and routing approaches derive a fitness function with the inclusion of multiple input parameters. The proposed IDTOMHR model was experimentally validated for its performance under different aspects. The extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.

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Artificial intelligence enabled intrusion detection systems for cognitive cyber-physical systems in industry 4.0 environment

Cited by 54 publications

References 27 publications

KHO-XAI: Krill herd optimization and Explainable Artificial Intelligence framework for Network Intrusion Detection Systems in Industry 4.0

KHO-XAI: Krill herd optimization and Explainable Artificial Intelligence framework for Network Intrusion Detection Systems in Industry 4.0

Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

Metaheuristics Enabled Clustering with Routing Scheme for Wireless Sensor Networks

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