Stacked one-class broad learning system for intrusion detection in industry 4.0
Abstract
With the vigorous development of Industry 4.0, industrial Big Data has turned into the core element of the Industrial Internet of Things. As one of the most fundamental and indispensable components in industrial cyber-physical systems (CPS), intelligent anomaly detection is still an essential and challenging issue. However, with the development of the network, there may exist unknown types of attacks, which are difficult to collect. Facing one-class industrial intrusion detection scenario that the collected training data only includes normal state, the one-class broad learning system (OCBLS) and the stacked OCBLS (ST-OCBLS) algorithms are developed. Benefiting from the characteristics of BLS, our proposed approaches retain the advantage of efficient training process. Moreover, the high-level hidden features of the network traffic data can be learned through the progressive encoding and decoding mechanism in ST-OCBLS. Extensive comparative experiments on several real-world intrusion detection tasks are carried out to demonstrate that our proposed methods have competitive performance and high efficiency in the face of complex network data and diversified types of intrusions. Overall, this article provides a new alternative solution for network intrusion detection in Industry 4.0.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In communication-centric attack scenarios, coarse-grained traffic statistics and entropy measures are sufficient to expose abnormal behavior without requiring deep protocol parsing.
“In these scenarios, coarse-grained traffic statistics and entropy measures are sufficient to expose abnormal behavior without requiring deep protocol parsing [13,21,28,32].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [28]
Deep architectures such as DAE, DAGMM, GCNN, and LSTM-based hybrids are mainly beneficial when modeling nonlinear feature interactions or temporal dependencies, but they can be sensitive to preprocessing choices and class imbalance, sometimes exhibiting complementary false-positive/false-negative patterns compared with simpler models.
“Deep architectures such as DAE, DAGMM, GCNN, and LSTM-based hybrids are mainly beneficial when modeling nonlinear feature interactions or temporal dependencies [13,15,17,21,23,29], but they can be sensitive to preprocessing choices and class imbalance, sometimes exhibiting complementary false-positive/falsenegative patterns compared with simpler models [20,21,28].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [28]
Classical machine-learning classifiers often perform competitively on high-dimensional statistical feature spaces derived from NSL-KDD, UNSW-NB15, NF-BoT-IoT, IoT-23, and similar benchmarks.
“Third, in terms of model behavior, the comparison across studies indicates that classical machine-learning classifiers often perform competitively on high-dimensional statistical feature spaces derived from NSL-KDD, UNSW-NB15, NF-BoT-IoT, IoT-23, and similar benchmarks [17,20,23,28,32,34].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [28]
Supervised statistical classifiers tend to achieve high accuracy when ample labeled attack data are available, whereas unsupervised and semi-supervised methods offer better robustness to previously unseen or zero-day attacks at the expense of slightly lower nominal accuracy.
“Supervised statistical classifiers tend to achieve high accuracy when ample labeled attack data are available [13,15,17,20,23,25,32,34], whereas unsupervised and semi-supervised methods offer better robustness to previously unseen or zero-day attacks at the expense of slightly lower nominal accuracy [17,20,21,25,28,34].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [28]
Cites
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892 · see citing works
The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
Deng, L. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2211477 · Cited by 4,858 · see citing works
Moustafa, N., & Slay, J. (2015) · 2015 Military Communications and Information Systems Conference (MilCIS) · doi:10.1109/milcis.2015.7348942 · Cited by 4,033 · see citing works
Support Vector Data Description
Tax, D. M. J., & Duin, R. P. W. (2003) · Machine Learning · doi:10.1023/b:mach.0000008084.60811.49 · Cited by 3,553 · see citing works
Isolation-Based Anomaly Detection
Liu, F. T., Ting, K. M., & Zhou, Z. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2133360.2133363 · Cited by 2,112 · see citing works
Chen, C. L. P., & Liu, Z. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2716952 · Cited by 1,957 · see citing works
Ruff, L., Vandermeulen, R. A., Görnitz, N., Deecke, L., Siddiqui, S. A., Binder, A., Müller, E., & Kloft, M. (2018) · International Conference on Machine Learning · Cited by 1,719 · see citing works
A survey of network anomaly detection techniques
Ahmed, M., Mahmood, A., & Hu, J.-K. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.11.016 · Cited by 1,708 · see citing works
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324 · see citing works
Intrusion detection by machine learning: A review
Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y., & Lin, W.-Y. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.05.029 · Cited by 969 · see citing works
Revathi, S., & Malathi, A. (2013) · Cited by 413 · see citing works
Variational LSTM Enhanced Anomaly Detection for Industrial Big Data
Zhou, X., Hu, Y., Liang, W., Ma, J., & Jin, Q. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3022432 · Cited by 403 · see citing works
A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU
Bhattacharya, S., Siva Rama Krishnan, S., Maddikunta, P. K. R., Kaluri, R., Singh, S., Gadekallu, T. R., Alazab, M., & Tariq, U. (2020) · Electronics · doi:10.3390/electronics9020219 · Cited by 364 · see citing works
Zhou, X., Liang, W., Shimizu, S., Ma, J., & Jin, Q. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3047675 · Cited by 349 · see citing works
Javed, A. R., Rehman, S. U., Khan, M. U., Alazab, M., & Gadekallu, T. R. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3059881 · Cited by 335 · see citing works
A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis
Tan, Z., Jamdagni, A., He, X., Nanda, P., & Liu, R. P. (2013) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2013.146 · Cited by 314 · see citing works
Recurrent Broad Learning Systems for Time Series Prediction
Xu, M., Han, M., Chen, C. L. P., & Qiu, T. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2863020 · Cited by 286 · see citing works
Liang, W., Li, K., Long, J., Kui, X., & Zomaya, A. Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2946791 · Cited by 207 · see citing works
Distributed Online One-Class Support Vector Machine for Anomaly Detection Over Networks
Miao, X., Liu, Y., Zhao, H., & Li, C. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2804940 · Cited by 187 · see citing works
LSCP: Locally Selective Combination in Parallel Outlier Ensembles
Zhao, Y., Nasrullah, Z., Hryniewicki, M. K., & Li, Z. (2019) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975673.66 · Cited by 154 · see citing works
One-Class Classification with Extreme Learning Machine
Leng, Q., Qi, H., Miao, J., Zhu, W., & Su, G. (2015) · Mathematical Problems in Engineering · doi:10.1155/2015/412957 · Cited by 147 · see citing works
Online and Scalable Unsupervised Network Anomaly Detection Method
Dromard, J., Roudière, G., & Owezarski, P. (2016) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2016.2627340 · Cited by 142 · see citing works
Genetic Algorithm to Improve SVM Based Network Intrusion Detection System
Kim, D. S., Nguyen, H.-N., & Park, J. S. (2005) · doi:10.1109/aina.2005.191 · Cited by 125 · see citing works
Nader, P., Honeine, P., & Beauseroy, P. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2330796 · Cited by 122 · see citing works
Incremental Weighted Ensemble Broad Learning System for Imbalanced Data
Yang, K., Yu, Z., Chen, C. L. P., Cao, W., You, J., & Wong, H. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3061428 · Cited by 114 · see citing works
Self-citationMachine Learning Techniques for Classifying Network Anomalies and Intrusions
Li, Z., Rios, A. L. G., Xu, G., & Trajković, L. (2019) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2019.8702583 · Cited by 78 · see citing works
Multilayer one-class extreme learning machine
Dai, H., Cao, J., Wang, T., Deng, M., & Yang, Z. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.03.004 · Cited by 75 · see citing works
Sarvari, S., Sani, N. F. M., Hanapi, Z. M., & Abdullah, M. T. (2020) · IEEE Access · doi:10.1109/access.2020.2986217 · Cited by 73 · see citing works
Cognitive Memory-Guided AutoEncoder for Effective Intrusion Detection in Internet of Things
Lu, H., Wang, T., Xu, X., & Wang, T. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3102637 · Cited by 73 · see citing works
Histogram-Based Intrusion Detection and Filtering Framework for Secure and Safe In-Vehicle Networks
Derhab, A., Belaoued, M., Mohiuddin, I., Kurniawan, F., & Khan, M. K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3088998 · Cited by 70 · see citing works
Yang, Q., Liu, G., Bao, Y., & Chen, Q. (2021) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2021.3127213 · Cited by 55 · see citing works
Self-citationLu, Y., Su, R., Zhang, C., & Qiao, L. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3111219 · Cited by 49 · see citing works
A Data-Mining Compensation Approach for Yaw Misalignment on Wind Turbine
Bao, Y., & Yang, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3065702 · Cited by 38 · see citing works
Self-citationClassification of BGP anomalies using decision trees and fuzzy rough sets
Li, Y., Xing, H.-J., Hua, Q., Wang, X.-Z., Batta, P., Haeri, S., & Trajkovic, L. (2014) · 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc.2014.6974096 · Cited by 36 · see citing works
Comparison of Machine Learning Algorithms for Detection of Network Intrusions
Li, Z., Batta, P., & Trajković, L. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00719 · Cited by 30 · see citing works
Detecting Network Anomalies and Intrusions in Communication Networks
Rios, A. L. G., Li, Z., Xu, G., Alonso, A. D., & Trajković, L. (2019) · IEEE International Conference on Intelligent Engineering Systems (INES) · doi:10.1109/ines46365.2019.9109448 · Cited by 15 · see citing works
Li, Z., Ding, Q., Haeri, S., & Trajković, L. (2018) · Advances in information security · doi:10.1007/978-3-319-73951-9_4 · Cited by 12 · see citing works
Pruning Broad Learning System based on Adaptive Feature Evolution
Liu, Y., Yang, K., Yu, Z., Liu, Z., Shi, Y., & Chen, C. L. P. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533681 · Cited by 8 · see citing works
Self-citationRobust Maximum Mixture Correntropy Criterion Based One-Class Classification Algorithm
Wang, T., Cao, J., Dai, H., Lei, B., & Zeng, H. (2021) · IEEE Intelligent Systems · doi:10.1109/mis.2021.3122958 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
1 of the 41 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
A survey on imbalanced learning: latest research, applications and future directions
Chen, W., Yang, K., Yu, Z., Shi, Y., & Chen, C. L. P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10759-6 · Cited by 535 · see citing works
Self-citationSivamohan, S., & Sridhar, S. S. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08319-0 · Cited by 99 · see citing works
Zhu, C., Pu, Y., Yang, K., Yang, Q., & Chen, C. L. P. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3277937 · Cited by 57 · see citing works
Self-citationDeep Cross-Modal Hashing Based on Semantic Consistent Ranking
Liu, X., Zeng, H., Shi, Y., Zhu, J., Hsia, C., & Ma, K. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3254199 · Cited by 58 · see citing works
Self-citationIndustrial cyber-physical systems protection: A methodological review
Canonico, R., & Sperlí, G. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103531 · Cited by 62 · see citing works
Machine learning-inspired intrusion detection system for IoT: Security issues and future challenges
Ahanger, T. A., Ullah, I., Algamdi, S. A., & Tariq, U. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110265 · Cited by 53 · see citing works
Anomaly classification in industrial Internet of things: A review
Rodríguez, M., Tobón, D. P., & Múnera, D. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200232 · Cited by 35 · see citing works
Twin Broad Learning System for Fault Diagnosis of Rotating Machinery
Yang, L., Yang, Z., Song, S., Li, F., & Chen, C. L. P. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3259022 · Cited by 30 · see citing works
Hybrid Ensemble Broad Learning System for Network Intrusion Detection
Lin, M., Yang, K., Yu, Z., Shi, Y., & Chen, C. L. P. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3332957 · Cited by 31 · see citing works
Self-citationBroad Learning Autoencoder With Graph Structure for Data Clustering
Yu, Z., Zhong, Z., Yang, K., Cao, W., & Chen, C. L. P. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3283425 · Cited by 28 · see citing works
Self-citationLocalizing and tracking of in-pipe inspection robots based on distributed optical fiber sensing
Zhu, C., Pu, Y., Yang, Y., Lyu, Z., Li, C., & Yang, Q. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102424 · Cited by 29 · see citing works
Self-citationLou, S., Yang, C., Kong, L., Lin, T., Zhang, H., Wu, P., & Chai, L. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3487776 · Cited by 30 · see citing works
A systematic review of anomaly detection in IoT security: towards quantum machine learning approach
Aparcana-Tasayco, A. J., Deng, X., & Park, J. H. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00414-6 · Cited by 38 · see citing works
Ensemble Denoising Autoencoders Based on Broad Learning System for Time-Series Anomaly Detection
Lin, Y., Yu, Z., Yang, K., & Chen, C. L. P. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3548941 · Cited by 27 · see citing works
Self-citationA Cost-Sensitive Machine Learning Model With Multitask Learning for Intrusion Detection in IoT
Telikani, A., Esmi, N., Soleymanpour, S., Shahbahrami, A., Shen, J., Gaydadjiev, G. N., & Hassanpour, R. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3314208 · Cited by 27 · see citing works
Two-Stage Intrusion Events Recognition for Vibration Signals From Distributed Optical Fiber Sensors
Lyu, Z., Zhu, C., Pu, Y., Chen, Z., Yang, K., & Yang, Q. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3336173 · Cited by 25 · see citing works
Self-citationShi, L., Shi, H., Ma, Z., Yan, S., & Zhou, Y. F. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3369713 · Cited by 24 · see citing works
When Broad Learning System Meets Label Noise Learning: A Reweighting Learning Framework
Liu, L., Chen, J., Yang, B., Feng, Q., & Chen, C. L. P. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3317255 · Cited by 16 · see citing works
Li, C., Hu, Q., Zhao, S., Wu, J., & Xiong, J. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107508 · Cited by 16 · see citing works
Li, C., Hu, Q., Zhao, S., Wu, J., Xiong, J., & Zhang, Q. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3337120 · Cited by 15 · see citing works
Network Intrusion Detection for Modern Smart Grids Based on Adaptive Online Incremental Learning
Lu, Q., An, K., Li, J., & Wang, J. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3535949 · Cited by 13 · see citing works
Fast Attack Detection Method for Imbalanced Data in Industrial Cyber-Physical Systems
Huang, M., Li, T., Li, B., Zhang, N., & Huang, H. (2023) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2023-0017 · Cited by 12 · see citing works
Cyber-Physical AI: Systematic Research Domain for Integrating AI and Cyber-Physical Systems
Lee, S., Chae, J., Jeon, H., Kim, T., Hong, Y.-G., Um, D., Kim, T., & Park, K. (2025) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3721437 · Cited by 20 · see citing works
Zhu, C., Yang, Y., Yang, K., Zhang, H., Yang, Q., & Chen, C. L. P. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i20.30264 · Cited by 12 · see citing works
Self-citationSeo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15 · see citing works
Lee, S., Chen, Z., Luo, Y., Li, X., Lu, M., Huang, Z. H., & Han Huang, A. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02532-x · Cited by 11 · see citing works
Transferable and discriminative broad network for unsupervised domain adaptation
Zhang, L., Yu, Z., Yang, K., Wang, B., & Chen, C. L. P. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113297 · Cited by 12 · see citing works
Self-citationDaily natural gas load forecasting based on sequence autocorrelation
Xiang, X., Shen, J., Yang, K., Zhang, G., Qian, J., & Zhu, C. (2022) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac57282.2022.10023872 · Cited by 11 · see citing works
Self-citationGu, Z., Shen, Y., Wang, Z., Jiang, Y., Chun, H., & Li, P. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3384597 · Cited by 10 · see citing works
Huang, M., Li, W., He, J., Lan, X., Li, T., & Zhang, N. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00498-5 · Cited by 10 · see citing works
Boztaş, G., Tuncer, T., Aydoğmuş, Ö., & Yıldırım, M. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109541 · Cited by 10 · see citing works
Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning
Lin, Y., Yu, Z., Yang, K., Fan, Z., & Chen, C. L. P. (2024) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2024.124557 · Cited by 11 · see citing works
Self-citationIntrusion detection of manifold regularized broad learning system based on LU decomposition
Liu, Y., Zhang, K., & Wang, Z. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05403-z · Cited by 9 · see citing works
DHR-BLS: A Huber-type robust broad learning system with its distributed version
Zhang, Y., Ke, S., Li, J., Liu, W., Hu, J., & Yang, K. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113184 · Cited by 9 · see citing works
Self-citationTowards Efficient Anomaly Detection Using Memory Broad Learning System
Zhong, Z., Yang, K., Yu, Z., Shi, Y., & Chen, C. L. P. (2023) · IEEE International Conference on Control Science and Systems Engineering (ICCSSE) · doi:10.1109/iccsse59359.2023.10244956 · Cited by 8 · see citing works
Self-citationAlternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis
Zhong, Y., Liu, K., Gao, S., & Luo, X. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3420735 · Cited by 8 · see citing works
Chu, Y., Guo, Y. L., Ding, W., Cao, H., & Ping, P. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130337 · Cited by 9 · see citing works
Improved Cross-Modal Retrieval Systems Using Self-Reinforcement and Quadruplet Alignment Hashing
Liu, X., Yu, Z., Jiang, J., Wang, B., Zhu, F., Chen, X., & Pedrycz, W. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3603210 · Cited by 6 · see citing works
Self-citationHe, N., Duan, J., & Lyu, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128638 · Cited by 6 · see citing works
Cybersecurity Enhancement in IoT Wireless Sensor Networks using Machine Learning
Ahmar, E. E., Rachini, A. S., & Attar, H. H. (2024) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2024.21.43 · Cited by 6 · see citing works
Broad Multitask Learning System With Group Sparse Regularization
Huang, J., Chen, C., Vong, C., & Cheung, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3416191 · Cited by 5 · see citing works
Single-layer feedforward neural networks with dynamic width for domain adaptation
Yang, L., Yang, Z., Li, F., & Chen, C. L. P. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4273-8 · Cited by 5 · see citing works
Zhao, K., Zhang, Z., Choo, K. R., Zhang, Z., & Zhang, T. (2023) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3637554 · Cited by 5 · see citing works
Observability Guaranteed Distributed Intelligent Sensing for Industrial Cyber-Physical System
Ji, Z., Chen, C., & Guan, X. (2024) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2024.3490838 · Cited by 4 · see citing works
Zhu, C., Pu, Y., Lyu, Z., Qian, J., & Yang, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44204-9_22 · Cited by 4 · see citing works
Self-citationGeneralized sparse and outlier-robust broad learning systems for multi-dimensional output problems
Zhang, Y., Dai, Y., Ke, S., Wu, Q., & Li, J. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120876 · Cited by 4 · see citing works
Multifactory Remanufacturing Process Optimization Considering Worker Scheduling
Zhou, L., Guo, X., Liu, Q., Wang, J., Qin, S., & Qi, L. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3570435 · Cited by 3 · see citing works
LCT-IDS: A Lightweight Transformer-Based Architecture for IoT Intrusion Detection
Tao, X., Du, Y., Jiang, Q., Zhao, W., Xu, K., & Meng, X. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3686866 · Cited by 3 · see citing works
Ensemble Approaches for Dynamic Data Stream Classification Under Label Scarcity
Yu, Z., Huang, S., Yang, K., Lv, J., & Chen, C. L. P. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3570072 · Cited by 3 · see citing works
Self-citationCategory-Constrained Broad Recurrent System for Cloud Anomaly Detection
Yao, X., Liu, F., Jiang, J., Zhong, G., & Chen, C. L. P. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2025.3576927 · Cited by 3 · see citing works
RCBLS: An Outlier-Robust Broad Learning Framework with Compact Structure
Guo, W., Yu, J., Zhou, C., Yuan, X., & Wang, Z. (2023) · Electronics · doi:10.3390/electronics12143118 · Cited by 3 · see citing works
Dayanand Lal, N., Adnan, M. M., Sutha Merlin, J., sri, K. R., & Palanivel, R. (2024) · International Conference on Distributed Computing and Optimization Techniques (ICDCOT) · doi:10.1109/icdcot61034.2024.10515662 · Cited by 3 · see citing works
Zhong, Y., Liu, K., Jiqiu, C., Xie, Z., & Li, W. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128440 · Cited by 2 · see citing works
Shallow and ensemble deep randomized neural network for anomaly detection
Kumari, A., Malik, A. K., Tanveer, M., & Suganthan, P. N. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108240 · Cited by 2 · see citing works
The Role of Anomaly Detection in Industry 4.0: A Survey of Techniques and Applications
Prasad, D. V., & Saraswathi, S. (2024) · Journal of Trends in Computer Science and Smart Technology · doi:10.36548/jtcsst.2024.2.003 · Cited by 2 · see citing works
Detecting Malicious Uniform Resource Locators Using an Applied Intelligence Framework
Oprea, S., & Bârã, A. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.051598 · Cited by 2 · see citing works
Ensemble Multi-Kernel Broad Learning System for Disease Diagnosis
Fan, Y., Yu, Z., Yang, K. X., Fan, Z., Sun, S. M., & Lv, J. (2024) · IEEE International Conference on Medical Artificial Intelligence (MedAI) · doi:10.1109/medai62885.2024.00020 · Cited by 1 · see citing works
Self-citationAdaptive Fuzzy Weighted Broad Network for Imbalanced Medical Diagnosis
Tang, L., Chen, W., Yu, Z., Wang, H., Lv, J., & Yang, K. X. (2024) · IEEE International Conference on Medical Artificial Intelligence (MedAI) · doi:10.1109/medai62885.2024.00026 · Cited by 1 · see citing works
Self-citationMulti-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection
Yang, Q., Chen, Y., & Shi, Y. (2025) · Applied Sciences · doi:10.3390/app15148094 · Cited by 1 · see citing works
Self-citationNazri, A., Agbolade, O., & Aziz, F. (2025) · IEEE Access · doi:10.1109/access.2025.3608032 · Cited by 1 · see citing works
Fan, J., Tan, S., Gu, H., Wang, Z., & Lü, J. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3674493 · Cited by 1 · see citing works
EDA-OCBLS: An error-distribution aware one-class broad learning system for anomaly detection
Tanveer, M., Mishra, A., Quadir, A., & Sajid, M. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109177 · Cited by 1 · see citing works
Liu, B., Zhang, Z., Hu, S., Sun, S., Liu, D., & Qiu, Z. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.048099 · Cited by 1 · see citing works
Zhang, J., Guo, R., Shi, Y., & Tang, W. (2024) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024271 · Cited by 1 · see citing works
Self-citationIntrusion Identification for Long-Distance Pipeline Based on Cloud-Edge Collaboration
Lyu, Z., Zhu, C., Pu, Y., Yang, K., Wu, A., & Yang, Q. (2024) · International Conference on Information Science and Technology · doi:10.1109/icist63249.2024.10805280 · Cited by 1 · see citing works
Self-citationAnomaly Detection based on Broad Leaning System for Rolling Element Bearing Fault Diagnosis
Yang, L., Yang, Z., Zheng, Z., He, L., Li, F., & Chen, C. L. P. (2022) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3544793.3560408
Liu, X., Chen, Y., Yang, B., Liu, P., & Wang, Z. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0154-7_2
Factorization-Based Broad Learning System With Time-Dependent Structure
Li, C., Liu, Z., He, X. Y., & Han, P. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3567757
Zhao, S., Guo, Z., Jiang, W., Lan, Y., & Ma, W. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.132234
MambaGuard: A Hybrid Framework for Unsupervised Anomaly Detection in IIoT Networks
Xiong, F., Mahanti, A., & Ding, N. (2025) · IEEE International Conference on Smart Internet of Things (SmartIoT) · doi:10.1109/smartiot66867.2025.00019
A collaborative intelligence framework for distributed security defense in Industry 4.0
Yang, L., Wang, J., Di, J., & Zhou, Z. (2026) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2026.108563
Li, X., Liu, X., Zou, X., Wei, D., Si, L., Dai, J., Zhu, C., & Pan, H. (2026) · Structural Health Monitoring · doi:10.1177/14759217261432558
Zhu, C., Pu, Y., Yang, Y., Ji, Y., Li, K., & Jiao, X. (2026) · Measurement · doi:10.1016/j.measurement.2026.121837
Deep random vector functional link network for anomaly detection
Ganaie, M. A., Uddin, N. S., Wasid, M., Imran, J., & Ali, R. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133112
Variance-constrained multi-view ensemble broad network for imbalanced data
Dong, Z., Chen, W., Yu, Z., Yang, K., & Chen, C. L. P. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109317
Self-citation
- Authors
- Yang, K., Shi, Y., Yu, Z., Yang, Q., Sangaiah, A. K., & Zeng, H.
- Affiliations
- Zhejiang University (CN)
- Huaqiao University (CN)
- South China University of Technology (CN)
- Vellore Institute of Technology University (IN)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2023-01
- Volume
- 19
- Issue
- 1
- Pages
- 251-260
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4226198717
- Cited by
- 75
- References
- 41
- Citations by year
- 2026: 8 · 2025: 20 · 2024: 26 · 2023: 19 · 2022: 2
- FWCI
- 7.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Natural Science Foundation of China (62106224, U21A20478, U21B2029)
- Scientific Research Funds of Huaqiao University
- Key-Area Research and Development Program of Guangdong Province (2018B010107002)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- intrusion detection, anomaly detection, Industry 4.0, Industrial Internet of Things, industrial cyber-physical systems, network traffic data, unknown attack detection, industrial big data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
