Using Kolmogorov–Arnold network for cyber-physical system security: A fast and efficient approach

KAN: Kolmogorov-Arnold NetworksPreprint

Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Marin Soljacic, A., Hou, T. Y., & Tegmark, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.19756 · Cited by 1,990 · see citing works

A Dataset to Support Research in the Design of Secure Water Treatment Systems

Goh, J., Adepu, S., Junejo, K. N., & Mathur, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-71368-7_8 · Cited by 729 · see citing works

A Survey of Cyber Attacks on Cyber Physical Systems: Recent Advances and Challenges

Duo, W., Zhou, M., & Abusorrah, A. M. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105548 · Cited by 599 · see citing works

WADI

Ahmed, C. M., Palleti, V. R., & Mathur, A. P. (2017) · International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater) · doi:10.1145/3055366.3055375 · Cited by 574 · see citing works

Cyber-physical systems and their security issues

Alguliyev, R. M., İmamverdiyev, Y., & Sukhostat, L. V. (2018) · Computers in Industry · doi:10.1016/j.compind.2018.04.017 · Cited by 479 · see citing works

Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning

Inoue, J., Yamagata, Y., Chen, Y., POSKITT, C. M., & Sun, J. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.149 · Cited by 372 · see citing works

An Ensemble Deep Learning-Based Cyber-Attack Detection in Industrial Control System

Al-Abassi, A., Karimipour, H., Dehghantanha, A., & Parizi, R. M. (2020) · IEEE Access · doi:10.1109/access.2020.2992249 · Cited by 323 · see citing works

Kolmogorov-Arnold Networks (KANs) for Time Series Analysis

Vaca-Rubio, C. J., Blanco, L., Pereira, R., & Caus, M. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11100692 · Cited by 279 · see citing works

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Efficient cyber attack detection in industrial control systems using lightweight neural networks and pca

Kravchik, M., & Shabtai, A. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3050101 · Cited by 224 · see citing works

Wav-KAN: Wavelet Kolmogorov-Arnold NetworksPreprint

Bozorgasl, Z., & Chen, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4835325 · Cited by 204 · see citing works

KAN-ODEs: Kolmogorov–Arnold network ordinary differential equations for learning dynamical systems and hidden physics

Koenig, B. C., Kim, S., & Deng, S. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117397 · Cited by 186 · see citing works

CARLA: Self-supervised contrastive representation learning for time series anomaly detection

Darban, Z. Z., Webb, G. I., Pan, S., Aggarwal, C. C., & Salehi, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110874 · Cited by 165 · see citing works

Bibliographical review on cyber attacks from a control oriented perspective

Sánchez, H. S., Rotondo, D., Escobet, T., Puig, V., & Quevedo, J. (2019) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2019.08.002 · Cited by 144 · see citing works

Cyber-Physical Systems

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A Dual-Isolation-Forests-Based Attack Detection Framework for Industrial Control Systems

Elnour, M., Meskin, N., Khan, K. M., & Jain, R. K. (2020) · IEEE Access · doi:10.1109/access.2020.2975066 · Cited by 117 · see citing works

Evolutionary Adversarial Autoencoder for Unsupervised Anomaly Detection of Industrial Internet of Things

Zeng, G., Yang, Y.-W., Lu, K., Geng, G., & Weng, J. (2025) · IEEE Transactions on Reliability · doi:10.1109/tr.2025.3528256 · Cited by 109 · see citing works

Digital Twin-based Anomaly Detection in Cyber-physical Systems

Xu, Q., Ali, S., & Yue, T. (2021) · IEEE Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst49551.2021.00031 · Cited by 108 · see citing works

Anomaly Detection Dataset for Industrial Control Systems

Dehlaghi-Ghadim, A., Moghadam, M. H., Balador, A., & Hansson, H. (2023) · IEEE Access · doi:10.1109/access.2023.3320928 · Cited by 101 · see citing works

Detection of Cyberattacks in Industrial Control Systems Using Enhanced Principal Component Analysis and Hypergraph-Based Convolution Neural Network (EPCA-HG-CNN)

S., P., Krithivasan, K., S., P., & Sriram V.S., S. (2020) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2020.2977872 · Cited by 88 · see citing works

A literature review of IoT and CPS—What they are, and what they are not

Lesch, V., Züfle, M., Bauer, A., Iffländer, L., Krupitzer, C., & Kounev, S. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111631 · Cited by 78 · see citing works

CKAN: Convolutional Kolmogorov–Arnold Networks Model for Intrusion Detection in IoT Environment

Elaziz, M. A., Fares, I. A., & Aseeri, A. O. (2024) · IEEE Access · doi:10.1109/access.2024.3462297 · Cited by 74 · see citing works

fKAN: Fractional Kolmogorov–Arnold Networks with trainable Jacobi basis functions

Aghaei, A. A. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129414 · Cited by 68 · see citing works

Lightweight Long Short-Term Memory Variational Auto-Encoder for Multivariate Time Series Anomaly Detection in Industrial Control Systems

Fährmann, D., Damer, N., Kirchbuchner, F., & Kuijper, A. (2022) · Sensors · doi:10.3390/s22082886 · Cited by 59 · see citing works

KAGNNs: Kolmogorov-Arnold Networks meet Graph LearningPreprint

Bresson, R., Nikolentzos, G., Panagopoulos, G., Chatzianastasis, M., Pang, J., & Vazirgiannis, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2406.18380 · Cited by 59 · see citing works

Multi-Objective Discrete Extremal Optimization of Variable-Length Blocks-Based CNN by Joint NAS and HPO for Intrusion Detection in IIoT

Lu, K., Huang, J., Zeng, G., Chen, M.-R., Geng, G., & Weng, J. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3545363 · Cited by 58 · see citing works

2014 IEEE International Conference on Automation, Quality and Testing, Robotics

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A Novel Interpretable Short-Term Load Forecasting Method Based on Kolmogorov-Arnold Networks

Jiang, B., Wang, Y., Wang, Q., & Geng, H. (2024) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2024.3498452 · Cited by 50 · see citing works

2023 The 8th International Conference on Computer and Communication Systems

(2023) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs57501.2023.10150648 · Cited by 44 · see citing works

Kolmogorov-Arnold Networks are Radial Basis Function NetworksPreprint

Li, Z. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2405.06721 · Cited by 42 · see citing works

Automated federated learning for intrusion detection of industrial control systems based on evolutionary neural architecture search

Shao, J., Zeng, G., Lu, K., Geng, G., & Weng, J. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103910 · Cited by 33 · see citing works

An explainable unsupervised anomaly detection framework for Industrial Internet of Things

Abudurexiti, Y., Han, G., Zhang, F., & Liu, L. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104130 · Cited by 33 · see citing works

A novel correlation feature self-assigned Kolmogorov-Arnold Networks for multi-energy load forecasting in integrated energy systems

Liu, X., Yang, Z., Guo, Y., Li, Z., & Xu, X. (2024) · Energy Conversion and Management · doi:10.1016/j.enconman.2024.119388 · Cited by 29 · see citing works

Robust Credit Card Fraud Detection Based on Efficient Kolmogorov-Arnold Network Models

Le, T.-T.-H., Hwang, Y., Kang, H., & Kim, H. (2024) · IEEE Access · doi:10.1109/access.2024.3485200 · Cited by 21 · see citing works

Quasi-Newton optimised Kolmogorov-Arnold Networks for wind farm power prediction

Mubarak, A. S., Ameen, Z. S., Mati, S., Lasisi, A., Naveed, Q. N., & Abdulkadir, R. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e40799 · Cited by 20 · see citing works

Classifying IoT Botnet Attacks With Kolmogorov-Arnold Networks: A Comparative Analysis of Architectural Variations

Hao, P., Le, T. D., Dinh, T. D., & Pham, V. D. (2025) · IEEE Access · doi:10.1109/access.2025.3528940 · Cited by 19 · see citing works

Comparative analysis of dimensionality reduction techniques for cybersecurity in the SWaT dataset

Bozdal, M., İleri, K., & Özkahraman, A. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05511-w · Cited by 17 · see citing works

Kolmogorov-Arnold TransformerPreprint

Yang, X., & Wang, X. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2409.10594 · Cited by 17 · see citing works

Abnormality Detection in Time-Series Bio-Signals using Kolmogorov-Arnold Networks (KANs) Based Models for Resource-Constrained DevicesPreprint

Huang, Z., Cui, J., Yu, L., Contreras, L. F. H., Dwivedi, G., & Kavehei, O. (2024) · medRxiv · doi:10.1101/2024.06.04.24308428 · Cited by 13 · see citing works

Strengthening ICS defense: Modbus-NFA behavior model for enhanced anomaly detection

Amer, E., Al‐rimy, B. A. S., & El–Sappagh, S. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.103990 · 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

Explainable Intrusion Detection in Industrial Control Systems

Eltomy, R., & Lalouani, W. (2024) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps59941.2024.10640024 · Cited by 12 · see citing works

Exploring Kolmogorov–Arnold Networks for Interpretable Time Series Classification

Barašin, I., Bertalanič, B., Mohorčič, M., & Fortuna, C. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/9553189 · Cited by 11 · see citing works

Unveiling the Power of Wavelets: A Wavelet-based Kolmogorov-Arnold Network for Hyperspectral Image ClassificationPreprint

Seydi, S. T., Bozorgasl, Z., & Chen, H. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2406.07869 · Cited by 8 · see citing works

Anomaly Detection for Process Industry using Class-Dependent Temperatures Loss

Li, Z., Yang, Z., Tang, D., Peng, S., & Li, J. (2023) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs57501.2023.10150495 · Cited by 7 · see citing works

Securing Critical Infrastructure: A Denoising Data-Driven Approach for Intrusion Detection in ICS Network

Izuazu, U. U., Ihekoronye, V. U., Kim, D.-S., & Lee, J. M. (2024) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic60209.2024.10463488 · Cited by 7 · see citing works

Securing Water Distribution Systems: Leveraging Sensor Networks Against Cyber-Physical Attacks Using Advanced Chicken Swarm Optimization

Lachure, J., & Doriya, R. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3472022 · Cited by 5 · see citing works

Secure IoT sensor networks through advanced anomaly detection with Kolmogorov–Arnold Networks (KANs)

Mishra, S., & Jain, U. (2025) · Microsystem Technologies · doi:10.1007/s00542-025-05848-7 · Cited by 5 · see citing works

Evaluating Machine Learning Techniques for ICS Security: Insights from Dataset Limitations and Classifier Performance

Amer, E., & Elboghdadly, T. (2024) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc62295.2024.10783517 · Cited by 5 · see citing works

Introducing Multi-Layer Concatenation as a Scheme to Combine Information in Water Distribution Cyber-Physical Systems

Vialatoux, C. F. , & Parrend, P. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.690 · Cited by 4 · see citing works

KAN-PDEs: A Novel Approach to Solving Partial Differential Equations Using Kolmogorov-Arnold Networks—Enhanced Accuracy and Efficiency

Yeo, S., Nguyen, P. A., Le, A. N., & Mishra, S. K. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-9112-5_3 · Cited by 4 · see citing works

Kolmogorov-Arnold Network for Machine Learning-based Motor Condition Diagnosis

Piedad, E., Rosario, C. A. D., Camaclang, R. C., Prieto‐Araujo, E., & Gomis‐Bellmunt, O. (2024) · International Conference on Electrical, Electronics and System Engineering (ICEESE) · doi:10.1109/iceese62315.2024.10828537 · Cited by 3 · see citing works

EPi-cKANs: Elasto-Plasticity Informed Kolmogorov-Arnold Networks Using Chebyshev PolynomialsPreprint

Mostajeran, F., & Faroughi, S. A. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.10897 · Cited by 2 · see citing works

Robust Automated Event Detection from Machine-Learning Analysis of Network Data

Rodda, M., Chambers, A. J., & Rohl, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.635 · Cited by 2 · see citing works

Detection of Cyber Attacks on a Water Distribution Testbed

Jia, M., & Zhang, P. (2022) · IEEE Conference on Control Technology and Applications (CCTA) · doi:10.1109/ccta49430.2022.9966085 · Cited by 2 · see citing works

AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review

Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2025) · Electronics · doi:10.3390/electronics15010020 · Cited by 15 · see citing works

Kolmogorov-Arnold networks for data-driven, physics-informed, and deep-operator learning: a review, synthesis, and new analysis

Faroughi, S. A., Mostajeran, F., Mashhadzadeh, A. H., & Faroughi, S. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108791 · Cited by 13 · see citing works

Enhancing real-time IoT intrusion detection using KAN-based frameworks with SMOTE

Mohammed, A. B., Chamseddine, E., & ElAdel, A. (2026) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2026.104461 · Cited by 9 · see citing works

Long-term error compensation in inertial IEKF-based localization with transformer-adaptive noise tuning and IMU missing data handling

Alikhani, M., & Nikoofard, A. (2025) · Measurement · doi:10.1016/j.measurement.2025.118928 · Cited by 5 · see citing works

Machine learning solutions with deep multilayer exogenous networks for distributed denial of service attacks model on networked resources in critical infrastructure

Zaeem, R. A., Chang, C., Khan, M. P., Shoaib, M., Shu, C., & Raja, M. A. Z. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112872 · Cited by 2 · see citing works

Quantum Annealed Geometric Mean Optimization for Calibrated Optimization of KAN

Mishra, A., & Panda, N. (2025) · International Conference on Intelligent Systems and Embedded Design (ISED) · doi:10.1109/ised67359.2025.11405218 · Cited by 2 · see citing works

Learning-aided fusion of SfM and IEKF for visual–inertial odometry

Alikhani, M., & Nikoofard, A. (2026) · Measurement · doi:10.1016/j.measurement.2026.121479 · Cited by 1 · see citing works

A Bio-Inspired Deep Learning Intrusion Detection Framework for Cyber-Physical System Security

Singh, S., Kubam, C. S., Modh, N. N., Zubair, K. M., & Ashanand (2025) · International Conference on Computational Intelligence, Security, and Artificial Intelligence (IntelliSecAI) · doi:10.1109/intellisecai66368.2025.11473168 · Cited by 1 · see citing works

An analytical solution for channel equalization using a Kolmogorov-Arnold network

Silva, L. C. B., Rocha, H. R. O., & Segatto, M. E. V. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ae1716

Attack Identification in Industrial Control Systems Using TabNet

Hassini, K., & Lazaar, M. (2025) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1109/sita67914.2025.11273377

Eliminating backdoor attacks on Vision Kolmogorov-Arnold networks via fine-function pruning

Tang, Y., Bo, Y. U. A. N., Chen, Z., Yang, J., Dai, T., & Chen, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115253

TEKAN: a temporally enhanced Kolmogorov-Arnold network for high-fidelity intrusion detection in industrial control systems

Priyanga, S., Subramanian, R. S., Ponnuviji, N. P., Lingham, N. S. R., Thilagam, T., Mishra, V., & Stephan, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-62079-0

Deployment-oriented defence-in-depth for legacy Industrial Control Systems under operational constraints: A systematic review of detection and compensating controls

Lara, R. C. H., & Lozano, M. A. (2026) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2026.100909

Integrated Sparse ISAR Imaging and Recognition via Hypernetwork-Guided Deep Unrolling and Multistage Dynamic Fusion

Ren, X., Li, X., Mingshan, W., Liu, L., Xueru, B., & Zhou, F. (2026) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2026.3740385

Design and Evaluation of Secure Telecommunications Architectures for Legacy Industrial Control Systems in Critical Infrastructure: A Doctoral Research Overview

Lara, R. C. H., & Lozano, M. A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-41327-7_16

IDSCor: A Hybrid Intrusion Detection System Framework for Industrial Networks

Anand, K., Geiger, J., Nataraj, S., Aman, M., Merz, S., Fiala, D., & Heigl, M. (2026) · International Conference on Advanced Machine Learning and Data Science (AMLDS) · doi:10.1109/amlds69611.2026.11723449

A Novel Approach for Intrusion Detection in Cyber-Physical Systems Using Decision Tree and Feature Dimensionality ReductionPreprint

Hashemian, N. S. (2025) · Research Square · doi:10.21203/rs.3.rs-6720475/v1

Lightweight Deep Learning Models for Intrusion Detection in Resource-Constrained Cyber-Physical Devices

Rathore, P. S. (2026) · Journal of Global Research in Multidisciplinary Studies(JGRMS) · doi:10.5281/zenodo.18278116

Detecting network intrusions in cyber-physical systems using deep autoencoder-based dimensionality reduction approach and deep neural networks

Ibor, A. E., Egete, D. O., Otiko, A. O., & Ashishie, D. U. (2025) · Journal of the Nigerian Society of Physical Sciences · doi:10.2139/ssrn.5306666 · Cited by 3 · see citing works

Deep Learning Techniques for Enhancing Cyber Physical Systems Security

Manjusha, P., Keerthana, C., Devasena, M., Karthik, B., & Anitha, C. (2026) · International Conference on Signal, Systems, and Computing for Next-Gen Automation (ICSSCNA) · doi:10.1109/icsscna68616.2026.11546775

Isolation Forest–Based Intrusion Detection for Cyber-Physical Systems

Oise, G. P., Konyeha, S., Uloko, F. O., Pius, K. C., Eferoba–Idio, E., Edobor, M. U., Mintah, E., Ukpebor, O., Sokoya, O., & Jessa, T. (2026) · Scientific Journal of Engineering Research · doi:10.64539/sjer.v2i2.2026.434 · Cited by 4 · see citing works