Self-Learning Spatial Distribution-Based Intrusion Detection for Industrial Cyber-Physical Systems

Chen, Jixiang; Miao, Hongyue; Song, Binjie; Lu, Yiqin; Pan, Weiqiang

doi:10.1109/tcss.2021.3135586

Cited by 19 publications

(11 citation statements)

References 34 publications

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“…Experiments showed that they proposed system had better accuracy in intrusion detection and classification. Gao [8] et al cleverly integrated the inter-class self-learning spatial distribution algorithm and cognitive computing into intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

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Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Lan

Huang

et al. 2023

Preprint

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Considering the robustness and accuracy of conventional intrusion detection models are easily influenced by adversarial attacks, this work proposes an anti-attack intrusion detection algorithm based on a message-passing neural network with traffic spatiotemporal features. Our algorithm can not only effectively distinguish and correlate upstream and downstream traffics, but also clearly embody the relationship between different traffics from the same source node and destination node by the established graph structure. We also improve the model by utilising the characteristics of the existing network attacks, which indicates thatthe traffic spatiotemporal characteristics could achieve high accuracy on attack traffic detection. Compared to the previous algorithms, extensive experiments show that our proposed model outperforms other similar models in performance. For the multi-classification tasks, our algorithm can effectively detect most of the network attacks and outperform traditional machine learning methods. In terms of algorithm robustness, our algorithm is less affected by adversarial attacks than traditional machine learning algorithms.

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Section: Related Workmentioning

confidence: 99%

“…Mchine learning has become the mainstream towards attack traffic detection in academics [1][2][3]. However, evergrowing problems have emerged with these methods.…”

Section: Introductionmentioning

confidence: 99%

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Lan

Huang

et al. 2023

Preprint

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“…Althobaiti et al [17] examine a novel cognitive computing-based IDS approach for achieving security from industrial CPS. The presented method contains pre-processing for discarding the noise which exists from the data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this work, probability that the x input vector (Layer 4) belonging to i-th class is determined in the above equation, where y represents the predicted class of input vector x. W and b denote the weight matrices and the bias vector, correspondingly, W j and W j denote the i rh and j rh row of matrixes W, correspondingly, b i and b j denote the i th and j th elements of vector b, correspondingly, and so f tmax refers to the nonlinearity function. The class with the maximum probability was assumed as the prediction label y pred of x input vector, as determined in Equation (17). The predictive error of sample dataset D(Loss(D)) is evaluated according to the true label, as demonstrated in Equation (18), where y i indicates the true label of x i .…”

Section: Hyperparameter Tuning Using Cso Algorithmmentioning

confidence: 99%

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

et al. 2022

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As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.

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“…The fusion of the virtual and physical world leads to advancement in the computing paradigm involving arti cial intelligence and other technologies [7]. There is a surge in connected networks because of society 5.0.…”

Section: Introductionmentioning

confidence: 99%

Detection Mechanism in IoT framework using Artificial Neural Networks

Jain¹,

Dhand

Sheoran

et al. 2023

Preprint

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Internet of Things (IoT) applications are now used more frequently due to the rapid expansion of wireless networking and the digital revolution. IoT helps in user-to-machine and machine-to-machine interaction. IoT objects have gained popularity because they can be accessed from anywhere. Healthcare, agriculture, smart cities, and the military are different domains where IoT objects are communicating with each other. The goal of anomaly-based techniques is to figure out which patterns are normal and which are aberrant. This approach of intrusion detection has the benefit of detecting original works of authorship intrusions. However, this technique has the drawback of frequently producing false positive results. To increase the effectiveness of anomaly-based intrusion detection methods, machine learning techniques are being evaluated. Anomaly-based intrusion detection techniques can be used by machine learning algorithms to watch active behavior and compare it to known intrusion footprints in order to stay aware of potential future attacks. In a hybrid approach, different identifying methods are combined in the same scheme. This technique will eliminate the weaknesses of a particular operation while improving the overall IoT system's reliability. In this research, we study intrusion-based systems using comparative analysis of several machine learning and deep learning algorithms. In the proposed work one hot encoding technique is used to deal with the categorical data. Different parameters like accuracy, F-1 score, precision, and recall value have been calculated. Experimental results prove that ANN yields 99.61% accuracy over other hybrid models. However, in Machine Learning, RandomForestClassifier yields the best results.

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Self-Learning Spatial Distribution-Based Intrusion Detection for Industrial Cyber-Physical Systems

Cited by 19 publications

References 34 publications

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

Detection Mechanism in IoT framework using Artificial Neural Networks

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