A hypergraph based Kohonen map for detecting intrusions over cyber–physical systems traffic

SMOTE synthetic minority over-sampling technique

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works

Individual Comparisons by Ranking Methods

Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354 · see citing works

Self-organized formation of topologically correct feature maps

Kohonen, T. (1982) · Biological Cybernetics · doi:10.1007/bf00337288 · Cited by 9,529 · see citing works

Pattern Recognition and Machine Learning

(2007) · Kybernetes · doi:10.1108/03684920710743466 · Cited by 8,540 · see citing works

The self-organizing map

Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414 · see citing works

Power Generation, Operation, and Control

Wood, A. J., & Wollenberg, B. F. (1984) · Cited by 8,337 · see citing works

Kernel Methods for Pattern Analysis

Shawe-Taylor, J. S., & Cristianini, N. (2004) · Cambridge University Press eBooks · doi:10.1017/cbo9780511809682 · Cited by 6,610 · see citing works

Robust Model-Based Fault Diagnosis for Dynamic Systems

Chen, J., & Patton, R. J. (1999) · ˜The œKluwer international series on Asian studies in computer and information science · doi:10.1007/978-1-4615-5149-2 · Cited by 4,273 · see citing works

Detection of Abrupt Changes: Theory and Application

Riedel, K. S. (1994) · Technometrics · doi:10.1080/00401706.1994.10485821 · Cited by 3,518 · see citing works

Deep Learning: Methods and Applications

Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480 · see citing works

Self-organized formation of topographically correct feature maps

Kohonen, T. (1982) · Biological Cybernetics · Cited by 2,995 · see citing works

Fault-Diagnosis Systems

Isermann, R. (2005) · Springer eBooks · doi:10.1007/3-540-30368-5 · Cited by 2,217 · see citing works

Attack Detection and Identification in Cyber-Physical Systems

Pasqualetti, F., Dörfler, F., & Bullo, F. (2013) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2013.2266831 · Cited by 2,139 · see citing works

A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

Yin, C., Zhu, Y., Fei, J., & He, X. (2017) · IEEE Access · doi:10.1109/access.2017.2762418 · Cited by 2,100 · see citing works

Survey of intrusion detection systems: Techniques, datasets and challenges

Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019) · Cybersecurity · doi:10.1186/s42400-019-0038-7 · Cited by 2,024 · see citing works

Deep Learning: Methods and Applications

Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,785 · see citing works

Guide to Industrial Control Systems (ICS) Security

Stouffer, K., Pillitteri, V., Lightman, S., Abrams, M., & Hahn, A. (2015) · doi:10.6028/nist.sp.800-82r2 · Cited by 1,377 · see citing works

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

Hwang, I., Kim, S., Kim, Y., & Seah, C. E. (2009) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2009.2026285 · Cited by 1,308 · see citing works

Survey of robust residual generation and evaluation methods in observer-based fault detection systems

Frank, P. M., & Ding, X. (1997) · Journal of Process Control · doi:10.1016/s0959-1524(97)00016-4 · Cited by 1,275 · see citing works

Deep packet: a novel approach for encrypted traffic classification using deep learning

Lotfollahi, M., Jafari Siavoshani, M., Shirali Hossein Zade, R., & Saberian, M. (2020) · Soft Computing · doi:10.1007/s00500-019-04030-2 · Cited by 1,190 · see citing works

BotMiner: clustering analysis of network traffic for protocol- and structure-independent botnet detection

Gu, G., Roberto Perdisci, A., Zhang, J., & Lee, W. (2008) · Journal of Bioresource Management · Cited by 1,033 · see citing works

Automation, Production Systems, and Computer-Integrated Manufacturing,

(2009) · Assembly Automation · doi:10.1108/aa.2009.03329dae.001 · Cited by 1,006 · see citing works

SVELTE: Real-time intrusion detection in the Internet of Things

Raza, S., Wallgren, L., & Voigt, T. (2013) · Ad Hoc Networks · doi:10.1016/j.adhoc.2013.04.014 · Cited by 908 · see citing works

A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems

Willsky, A. S., & Jones, H. (1976) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1976.1101146 · Cited by 880 · see citing works

Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism

He, Y., Mendis, G. J., & Wei, J. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2703842 · Cited by 844 · 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

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works

Very sparse random projections

Li, P., Hastie, T. J., & Church, K. (2006) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1150402.1150436 · Cited by 632 · see citing works

Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication

Çatalyürek, Ü. V., & Aykanat, C. (1999) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/71.780863 · Cited by 573 · see citing works

Analytical Redundancy Methods in Fault Detection and Isolation - Survey and Synthesis

Gertler, J. (1991) · IFAC Proceedings Volumes · doi:10.1016/s1474-6670(17)51119-2 · Cited by 521 · see citing works

Automation production systems and computer integrated manufacturing

Vámos, T. (1988) · Automatica · doi:10.1016/0005-1098(88)90106-9 · Cited by 507 · see citing works

Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes

Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., & Klette, R. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.02.006 · Cited by 471 · see citing works

Large-scale malware classification using random projections and neural networks

Dahl, G. E., Stokes, J. W., Deng, L., & Yu, D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638293 · Cited by 450 · see citing works

Distributed Intrusion Detection System in a Multi-Layer Network Architecture of Smart Grids

Zhang, Y., Wang, L., Sun, W., Green, R., & Alam, M. (2011) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2011.2159818 · Cited by 449 · see citing works

Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks

Goh, J., Adepu, S., Tan, M., & Lee, Z. S. (2017) · IEEE International Symposium on High Assurance Systems Engineering (HASE) · doi:10.1109/hase.2017.36 · Cited by 447 · see citing works

Guide to Industrial control Systems (ICS) security :

Stouffer, K. A., Falco, J. A., & Scarfone, K. A. (2008) · doi:10.6028/nist.sp.800-82e2008 · Cited by 373 · see citing works

Sequential Analysis: Hypothesis Testing and Changepoint Detection

Gillard, J. (2015) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12122_5 · Cited by 352 · see citing works

Cyber Security of Water SCADA Systems—Part I: Analysis and Experimentation of Stealthy Deception Attacks

Amin, S., Litrico, X., Sastry, S., & Bayen, A. M. (2012) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2012.2211873 · Cited by 350 · see citing works

Using Model-based Intrusion Detection for SCADA Networks

Cheung, S. W., Dutertre, B., Fong, M. W., Lindqvist, U., Skinner, K., & Valdes, A. (2006) · Cited by 321 · see citing works

Introduction to Computer Security

Mammeri, Z. (2024) · Cryptography · doi:10.1002/9781394207510.ch1 · Cited by 310 · see citing works

A novel approach to detection of intrusions in computer networks via adaptive sequential and batch-sequential change-point detection methods

Tartakovsky, A. G., Rozovskii, B. L., Blažek, R. B., & Kim, H. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.879308 · Cited by 292 · see citing works

Detection of abrupt changes. Theory and application

Rudas, I. J. (1994) · Control Engineering Practice · doi:10.1016/0967-0661(94)90196-1 · Cited by 290 · see citing works

Multi-level Anomaly Detection in Industrial Control Systems via Package Signatures and LSTM Networks

Feng, C., Li, T., & Chana, D. (2017) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2017.34 · Cited by 240 · see citing works

An efficient intrusion detection system based on hypergraph - Genetic algorithm for parameter optimization and feature selection in support vector machine

Raman, M. R. G., Somu, N., Kirthivasan, K., Liscano, R., & Sriram, V. S. S. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.07.005 · Cited by 239 · see citing works

Combating advanced persistent threats: From network event correlation to incident detection

Friedberg, I., Skopik, F., Settanni, G., & Fiedler, R. (2014) · Computers & Security · doi:10.1016/j.cose.2014.09.006 · Cited by 218 · see citing works

ANALYSIS OF SAMPLING TECHNIQUES FOR IMBALANCED DATA: AN N=648 ADNI STUDY

Dubey, R., Zhou, J.-Y., Wang, Y.-L., Thompson, P., & Ye, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.005 · Cited by 204 · see citing works

An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications

Beaver, J. M., Hink, R. B., & Buckner, M. A. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.105 · Cited by 197 · see citing works

Untitled work

(2005) · Cited by 189 · see citing works

Statistical Learning Theory: Models, Concepts, and Results

Ulrike Von Luxburg, A., & Schölkopf, B. (2011) · Handbook of the history of logic · doi:10.1016/b978-0-444-52936-7.50016-1 · Cited by 189 · see citing works

On SCADA control system command and response injection and intrusion detection

Gao, W., Morris, T. H., Reaves, B., & Richey, D. (2010) · eCrime Researchers Summit (eCrime) · doi:10.1109/ecrime.2010.5706699 · Cited by 187 · see citing works

Cyber Security of Water SCADA Systems—Part II: Attack Detection Using Enhanced Hydrodynamic Models

Amin, S., Litrico, X., Sastry, S., & Bayen, A. M. (2012) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2012.2211874 · Cited by 156 · see citing works

Modbus/DNP3 State-Based Intrusion Detection System

Fovino, I. N., Carcano, A., Murel, T. D. L., Trombetta, A., & Masera, M. (2010) · International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2010.86 · Cited by 153 · see citing works

State observers for discrete-time linear systems with unknown inputs

Valcher, M. E. (1999) · IEEE Transactions on Automatic Control · doi:10.1109/9.746275 · Cited by 150 · see citing works

Graphs and Hypergraphs

Corsini, P., & Leoreanu, V. (2003) · Springer eBooks · doi:10.1007/978-1-4757-3714-1_3 · Cited by 131 · see citing works

<inline-formula><tex-math notation="TeX">${l_p}$</tex-math></inline-formula>-norms in One-Class Classification for Intrusion Detection in SCADA Systems

Nader, P., Honeine, P., & Beauseroy, P. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2330796 · Cited by 122 · see citing works

Introduction to Computer Security

Anderson, H. (2003) · Cited by 110 · see citing works

Stealthy deception attacks on water SCADA systems

Amin, S., Litrico, X., Sastry, S., & Bayen, A. M. (2010) · HAL · Cited by 110 · see citing works

Intrusion Detection System for IEC 60870-5-104 based SCADA networks

Yang, Y., McLaughlin, K., Littler, T., Sezer, S., Pranggono, B., & Wang, H. (2013) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesmg.2013.6672100 · Cited by 103 · see citing works

Distributed Detection of Single-Stage Multipoint Cyber Attacks in a Water Treatment Plant

Adepu, S., & Mathur, A. P. (2016) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/2897845.2897855 · Cited by 95 · see citing works

An enhanced optimization based algorithm for intrusion detection in SCADA network

Selvarajan, S., & David, P. W. (2017) · Computers & Security · doi:10.1016/j.cose.2017.04.012 · Cited by 95 · see citing works

Safeguarding SCADA Systems with Anomaly Detection

Bigham, J., Gamez, D., & Lü, N. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45215-7_14 · Cited by 94 · see citing works

An Investigation into the Response of a Water Treatment System to Cyber Attacks

Adepu, S., & Mathur, A. P. (2016) · Proceedings - IEEE International Symposium on High-Assurance Systems Engineering/Proceedings · doi:10.1109/hase.2016.14 · Cited by 88 · see citing works

Using Process Invariants to Detect Cyber Attacks on a Water Treatment System

Adepu, S., & Mathur, A. P. (2016) · IFIP advances in information and communication technology · doi:10.1007/978-3-319-33630-5_7 · Cited by 88 · 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

k-way Hypergraph Partitioning via n-Level Recursive Bisection

Schlag, S., Henne, V., Heuer, T., Meyerhenke, H., Sanders, P., & Schulz, C. (2015) · doi:10.1137/1.9781611974317.5 · Cited by 80 · see citing works

Generalized Attacker and Attack Models for Cyber Physical Systems

Adepu, S., & Mathur, A. P. (2016) · IEEE Annual Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2016.122 · Cited by 76 · see citing works

Detecting cyberattacks in industrial control systems using online learning algorithms

Li, G., Shen, Y., Zhao, P., Lu, X., Liu, J., Liu, Y., & Hoi, S. C. H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.031 · Cited by 74 · see citing works

Unearthing Stealthy Program Attacks Buried in Extremely Long Execution Paths

Shu, X., Yao, D. D., & Ramakrishnan, N. (2015) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2810103.2813654 · Cited by 72 · see citing works

Cyber Stealth Attacks in Critical Information Infrastructures

Cazorla, L., Alcaraz, C., & López, J. (2016) · IEEE Systems Journal · doi:10.1109/jsyst.2015.2487684 · Cited by 72 · see citing works

Asymptotic performance of PCA for high-dimensional heteroscedastic data

Hong, D. K., Balzano, L., & Fessler, J. A. (2018) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2018.06.002 · Cited by 70 · see citing works

Safety and Security Interactions Modeling Using the BDMP Formalism: Case Study of a Pipeline

Kriaa, S., Bouissou, M., Colin, F., Halgand, Y., & Piètre-Cambacédès, L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10506-2_22 · Cited by 63 · see citing works

Evaluation of Anomaly Detection techniques for SCADA communication resilience

Shirazi, S. N., Gouglidis, A., Syeda, K. N., Simpson, S. V., Mauthe, A., Stephanakis, I. M., & Hutchison, D. (2016) · Resilience Week (RWS) · doi:10.1109/rweek.2016.7573322 · Cited by 56 · see citing works

Deep autoencoders as anomaly detectors: Method and case study in a distributed water treatment plant

Raman, M. R. G., Dong, W., & Mathur, A. P. (2020) · Computers & Security · doi:10.1016/j.cose.2020.102055 · Cited by 52 · see citing works

Stealthy deception attacks on water SCADA systems

Amin, S., Litrico, X., Sastry, S., & Bayen, A. M. (2010) · ACM International Conference on Hybrid Systems: Computation and Control (HSCC) · doi:10.1145/1755952.1755976 · Cited by 52 · see citing works

AHW-BGOA-DNN: a novel deep learning model for epileptic seizure detection

Glory, H. A., Vigneswaran, C., Jagtap, S. S., Shruthi, R., Hariharan, G., & Sriram, V. S. S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05384-7 · Cited by 45 · see citing works

A Sharper Sense of Self: Probabilistic Reasoning of Program Behaviors for Anomaly Detection with Context Sensitivity

Xu, K., Tian, K., Yao, D. D., & Ryder, B. G. (2016) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2016.49 · Cited by 42 · see citing works

Towards A Stateful Analysis Framework for Smart Grid Network Intrusion Detection

Kang, B., McLaughlin, K., & Sezer, S. (2016) · Electronic workshops in computing · doi:10.14236/ewic/ics2016.14 · Cited by 40 · see citing works

Context-Awareness Using Anomaly-Based Detectors for Smart Grid Domains

Alcaraz, C., Cazorla, L., & Fernández, G. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-17127-2_2 · Cited by 39 · see citing works

Deep Self-Organizing Map for visual classification

Liu, N., Wang, J., & Gong, Y. (2015) · doi:10.1109/ijcnn.2015.7280357 · Cited by 37 · see citing works

Bloom filter based intrusion detection for smart grid SCADA

Parthasarathy, S., & Kundur, D. (2012) · IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) · doi:10.1109/ccece.2012.6334816 · Cited by 37 · see citing works

Artificial immune system based intrusion detection in a distributed hierarchical network architecture of smart grid

Zhang, Y., Wang, L., Sun, W., Green, R., & Alam, M. (2011) · IEEE Power Engineering Society General Meeting · doi:10.1109/pes.2011.6039697 · Cited by 37 · see citing works

STOCHASTIC FAULT DIAGNOSABILITY IN PARITY SPACES

Gustafsson, F. (2002) · IFAC Proceedings Volumes · doi:10.3182/20020721-6-es-1901.00738 · Cited by 35 · see citing works

Metaheuristic neural networks for anomaly recognition in industrial sensor networks with packet latency and jitter for smart infrastructures

Mansouri, A., Majidi, B., & Shamisa, A. (2018) · International Journal of Computers and Applications · doi:10.1080/1206212x.2018.1533613 · Cited by 32 · see citing works

A rough set-based hypergraph trust measure parameter selection technique for cloud service selection

Somu, N., Kirthivasan, K., & Sriram, V. S. S. (2017) · The Journal of Supercomputing · doi:10.1007/s11227-017-2032-8 · Cited by 31 · see citing works

Host-based anomaly detection for pervasive medical systems

Asfaw, B., Bekele, D., Eshete, B., Villafiorita, A., & Weldemariam, K. (2010) · International Conference on Risks and Security of Internet and Systems (CRiSIS) · doi:10.1109/crisis.2010.5764923 · Cited by 24 · see citing works

Automation is a breeze with AutoIt

Brand, J., & Balvanz, J. L. (2005) · ACM SIGUCCS Conference on User Services (SIGUCCS) · doi:10.1145/1099435.1099439 · Cited by 23 · see citing works

DeepOrigin: End-To-End Deep Learning For Detection Of New Malware Families

Cordonsky, I., Rosenberg, I., Sicard, G., & David, E. O. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489667 · Cited by 22 · see citing works

End-to-End Adversarial Learning for Intrusion Detection in Computer Networks

Mohammadi, B., & Sabokrou, M. (2019) · Conference on Local Computer Networks · doi:10.1109/lcn44214.2019.8990759 · Cited by 22 · see citing works

Sequential monitoring of SCADA systems against cyber/physical attacks ★ ★This work received financial support from the French National Research Agency (ANR).

van Long, Fillatre, L., & Nikiforov, I. V. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.09.616 · Cited by 20 · see citing works

MOLESTRA: A Multi-Task Learning Approach for Real-Time Big Data Analytics

Demertzis, K., Iliadis, L. A., & Anezakis, V.-D. (2018) · doi:10.1109/inista.2018.8466306 · Cited by 19 · see citing works

Sequential monitoring of SCADA systems against cyber/physical attacks

van Long, Fillatre, L., & Nikiforov, I. V. (2015) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 17 · see citing works

Effective Defence Against Zero-Day Exploits Using Bayesian Networks

Li, T., & Hankin, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71368-7_11 · Cited by 16 · see citing works

k-way Hypergraph Partitioning via n-Level Recursive BisectionPreprint

Schlag, S., Henne, V., Heuer, T., Meyerhenke, H., Sanders, P., & Schulz, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03137 · Cited by 11 · see citing works

Isomorphism problems for hypergraphs

Berge, C. (1974) · Lecture notes in mathematics · doi:10.1007/bfb0066174 · Cited by 9 · see citing works

Unsupervised bin-wise pre-training: A fusion of information theory and hypergraph

Glory, H. A., Vigneswaran, C., & Sriram, V. S. S. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105650 · Cited by 8 · see citing works

Modelling Cost-Effectiveness of Defenses in Industrial Control Systems

Fielder, A., Li, T., & Hankin, C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45477-1_15 · Cited by 7 · see citing works

Classification mono-classe pour la détection des cyber-intrusions dans les systèmes industriels

Nader, P. (2015) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 6 · see citing works

Promises and delivery—a research imperative for new approaches to medical education

Prideaux, D. J. (2004) · BMJ · doi:10.1136/bmj.329.7461.331 · Cited by 5 · see citing works

Helly hypergraph based matching framework using deterministic sampling techniques for spatially improved point feature based image matching

Divya Lakshmi, K., Muthaiah, R., Krithivasan, K., & Roy, D. S. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6852-1 · Cited by 5 · see citing works

Subtree Hypergraph-Based Attack Detection Model for Signature Matching over SCADA HMI

Jagtap, S. S., & Sriram, V. S. S. (2019) · Communications in computer and information science · doi:10.1007/978-981-15-0871-4_13 · Cited by 5 · see citing works

Artificial intelligence for cybersecurity: Literature review and future research directions

Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101804 · Cited by 615 · see citing works

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey, N., Tan, Q., & Villar, J. R. (2023) · Electronics · doi:10.3390/electronics12153283 · Cited by 160 · see citing works

Comparative Evaluation of AI-Based Techniques for Zero-Day Attacks Detection

Ali, S., Rehman, S. U., Imran, A., Adeem, G., Iqbal, Z., & Kim, K. (2022) · Electronics · doi:10.3390/electronics11233934 · Cited by 118 · see citing works

Enhancing IIoT networks protection: A robust security model for attack detection in Internet Industrial Control Systems

Khan, I. A., Keshk, M., Pi, D., Khan, N., Hussain, Y., & Soliman, H. (2022) · Ad Hoc Networks · doi:10.1016/j.adhoc.2022.102930 · Cited by 110 · see citing works

Digitalisation and Innovation in the Steel Industry in Poland—Selected Tools of ICT in an Analysis of Statistical Data and a Case Study

Gajdzik, B., & Wolniak, R. (2021) · Energies · doi:10.3390/en14113034 · Cited by 108 · see citing works

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

Alohali, M. A., Al‐Wesabi, F. N., Hilal, A. M., Goel, S. S., Gupta, D. K., & Khanna, A. K. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09780-8 · Cited by 105 · see citing works

Review of intrusion detection system in cyber‐physical system based networks: Characteristics, industrial protocols, attacks, data sets and challenges

Ji, R., Padha, D., Singh, Y. P., & Sharma, S. (2024) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.5029 · Cited by 45 · see citing works

TrIDS: an intelligent behavioural trust based IDS for smart healthcare system

Singh, A., Chatterjee, K., & Satapathy, S. C. (2023) · Cluster Computing · doi:10.1007/s10586-022-03614-2 · Cited by 20 · see citing works

Uncertainty‐informed regional deformation diagnosis of arch dams

Chen, X., Sun, W., Hu, S., Li, L., Gu, C., Guo, J., Wei, B., & Xu, B. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13395 · Cited by 16 · see citing works

Deep learning techniques to detect cybersecurity attacks: a systematic mapping study

Torre, D., Mesadieu, F., & Chennamaneni, A. (2023) · Empirical Software Engineering · doi:10.1007/s10664-023-10302-1 · Cited by 16 · see citing works

Design-knowledge in learning plant dynamics for detecting process anomalies in water treatment plants

Sung, D. C. L., Raman, M. R. G., & Mathur, A. P. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102532 · Cited by 14 · see citing works

Masked Graph Neural Networks for Unsupervised Anomaly Detection in Multivariate Time Series

Xu, K., Li, Y., Li, Y., Xu, L., Li, R., & Dong, Z. (2023) · Sensors · doi:10.3390/s23177552 · Cited by 13 · 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

Securing Industrial Control Systems From Cyber-Attacks: A Stacked Neural-Network-Based Approach

Jagtap, S. S., Sriram, V. S. S., Kotecha, K. V., & Subramaniyaswamy, V. (2022) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2022.3168997 · Cited by 11 · see citing works

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Ali, J. (2021) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit53391.2021.9677429 · Cited by 10 · see citing works

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Sobchuk, V., Zamrii, I., & Laptiev, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-20141-7_53 · Cited by 5 · see citing works

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Bidzilya, Y. M., Solomin, Y. O., Shapovalova, H. V., Georgiievska, V. V., & Poplavska, N. (2021) · Cuestiones Políticas · doi:10.46398/cuestpol.3970.16 · Cited by 4 · see citing works

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Sagar, M., & Vanmathi, C. (2022) · International Journal of Wireless and Microwave Technologies · doi:10.5815/ijwmt.2022.05.06 · Cited by 4 · see citing works

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Zhang, S., Luo, Z., Ren, Z., Zhu, Y., Zhang, H., & Liu, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114712 · Cited by 3 · see citing works

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Shrivastava, S., Raman, M. R. G., & Mathur, A. P. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671615 · Cited by 3 · see citing works

Machine learning for resilience analysis: a review of systems under cyberattacks

Yin, R., & Varga, L. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113542 · Cited by 2 · see citing works

Computational Intelligence Approaches for Analysis of the Detection of Zero-day Attacks

Rehman, S. U., Ali, S., Adeem, G., Hussain, S., & Raza, S. S. (2022) · University of Wah Journal of Science and Technology (UWJST) · doi:10.56220/uwjst.v6i0.136 · Cited by 2 · see citing works

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Priyanga, S., Pravinraj, S., Repalle, V. B., Krithivasan, K., & Sriram, V. S. S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-3734-9_27 · Cited by 2 · see citing works

AI-Assisted Consensus Mechanisms for Scalable Blockchain Networks

Jha, L. (2025) · Scientific Journal of Artificial Intelligence and Blockchain Technologies · doi:10.63345/sjaibt.v2.i2.301 · Cited by 2 · see citing works

Dynamic ST-based PCA method for adaptive data detection

Lin, T. (2022) · Advances in Mechanical Engineering · doi:10.1177/16878132221133155 · Cited by 1 · see citing works

Mathematical Mapping Study of Synthetic Antioxidants Behavior in Biodiesel: Application of the Self-Organized Feature Map (SOFM) and Multilayer Perceptron (MLP)

Clemente, M., Silva, H., Campos, J., Silva, N., Sousa, E., Mantovani, A. C. G., Angilelli, K. G., & Borsato, D. (2022) · Journal of the Brazilian Chemical Society · doi:10.21577/0103-5053.20220139 · Cited by 1 · see citing works

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Lu, Y., Yang, Q., & Kuang, Y. (2025) · International Journal of High Speed Electronics and Systems · doi:10.1142/s0129156425403298 · Cited by 1 · see citing works

Intrusion Detection in Cyber-Physical Systems Based on Petri Net

Ghazi, Z., & Doustmohammadi, A. (2018) · Information Technology And Control · doi:10.5755/j01.itc.47.2.16277 · Cited by 9 · see citing works

A Hybrid Machine Learning Approach for Intrusion Detection in Cyber-Physical Manufacturing Systems

Jithish, J., Sankaran, S., & Achuthan, K. (2024) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003406105-14 · Cited by 14 · see citing works

Untitled work

(2021) · doi:10.1007/978-3-030-78086-9_6 · Cited by 19 · see citing works

Hybrid deep architecture for intrusion detection in cyber‐physical system: An optimization‐based approach

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Alheeti, K. M. A., Majeed, S. H., Awaad, A. M., Najem, A. K. A. H., & Sakr, R. H. (2026) · International Conference on Smart Multidomain Integrated Learning Environments (ICSMILE) · doi:10.1109/icsmile69273.2026.11519122