{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T00:27:24Z","timestamp":1718411244538},"reference-count":30,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005632","name":"The National Centre for Research and Development","doi-asserted-by":"publisher","award":["CYBERSECIDENT\/488240\/IV\/NCBR\/2021"],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The article deals with the issue of detecting cyberattacks on control algorithms running in a real Programmable Logic Controller (PLC) and controlling a real laboratory control plant. The vulnerability of the widely used Proportional\u2013Integral\u2013Derivative (PID) controller is investigated. Four effective, easy-to-implement, and relatively robust methods for detecting attacks on the control signal, output variable, and parameters of the PID controller are researched. The first method verifies whether the value of the control signal sent to the control plant in the previous step is the actual value generated by the controller. The second method relies on detecting sudden, unusual changes in output variables, taking into account the inertial nature of dynamic plants. In the third method, a copy of the controller parameters is used to detect an attack on the controller\u2019s parameters implemented in the PLC. The fourth method uses the golden run in attack detection.<\/jats:p>","DOI":"10.3390\/s24123860","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T12:52:10Z","timestamp":1718369530000},"page":"3860","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Cyberattack Detection Methods in Industrial Control Systems"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6556-7919","authenticated-orcid":false,"given":"Piotr","family":"Marusak","sequence":"first","affiliation":[{"name":"Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1152-8690","authenticated-orcid":false,"given":"Robert","family":"Nebeluk","sequence":"additional","affiliation":[{"name":"Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6984-391X","authenticated-orcid":false,"given":"Andrzej","family":"Wojtulewicz","sequence":"additional","affiliation":[{"name":"Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5955-5890","authenticated-orcid":false,"given":"Krzysztof","family":"Cabaj","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0257-8255","authenticated-orcid":false,"given":"Patryk","family":"Chaber","sequence":"additional","affiliation":[{"name":"Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6846-2004","authenticated-orcid":false,"given":"Maciej","family":"\u0141awry\u0144czuk","sequence":"additional","affiliation":[{"name":"Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6610-8039","authenticated-orcid":false,"given":"Sebastian","family":"Plamowski","sequence":"additional","affiliation":[{"name":"Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8436-7325","authenticated-orcid":false,"given":"Krzysztof","family":"Zarzycki","sequence":"additional","affiliation":[{"name":"Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Toker, O., and Ozdemir, O. (2020, January 16\u201318). Physical-layer Cyberattack Resilient OFDM Automotive Radars. Proceedings of the 2020 IEEE Vehicular Networking Conference (VNC), New York, NY, USA.","DOI":"10.1109\/VNC51378.2020.9318366"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hou, B., Zeng, S., Xi, B., Jia, S., Guo, Q., Xu, L., and Sun, H. (2021, January 28\u201330). Performance of Neighborhood-Watch-Based Resilient Distributed Energy Management Algorithm Under Different Types of Cyberattacks. Proceedings of the 2021 IEEE 4th International Electrical and Energy Conference (CIEEC), Wuhan, China.","DOI":"10.1109\/CIEEC50170.2021.9510236"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zabetian-Hosseini, A., Mehrizi-Sani, A., and Liu, C.C. (2018, January 21\u201323). Cyberattack to Cyber-Physical Model of Wind Farm SCADA. Proceedings of the IECON 2018\u201444th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA.","DOI":"10.1109\/IECON.2018.8591200"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Colter, J., Kinnison, M., Henderson, A., Schlager, S.M., Bryan, S., O\u2019Grady, K.L., Abballe, A., and Harbour, S. (2022, January 18\u201322). Testing the Resiliency of Consumer Off-the-Shelf Drones to a Variety of Cyberattack Methods. Proceedings of the 2022 IEEE\/AIAA 41st Digital Avionics Systems Conference (DASC), Portsmouth, VA, USA.","DOI":"10.1109\/DASC55683.2022.9925879"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TSC.2020.2964548","article-title":"Buoy Sensor Cyberattack Detection in Offshore Petroleum Cyber-Physical Systems","volume":"13","author":"Mu","year":"2020","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1109\/TSG.2020.3040361","article-title":"A Deep Learning-Based Cyberattack Detection System for Transmission Protective Relays","volume":"12","author":"Khaw","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pazouki, S., Bibek, K.C., Alkhwaildi, H.A., and Asrari, A. (2021, January 11\u201313). Modelling of Smart Homes Affected by Cyberattacks. Proceedings of the 2020 52nd North American Power Symposium (NAPS), Tempe, AZ, USA.","DOI":"10.1109\/NAPS50074.2021.9449777"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Jiang, Y., Xu, A., Hong, C., and Chen, J. (2020, January 20\u201323). Method to Evaluate the Impact of Cyberattacks against Charging Piles on Distribution Network. Proceedings of the 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Nanjing, China.","DOI":"10.1109\/APPEEC48164.2020.9220574"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kbar, G., and Alazab, A. (2019, January 8\u20139). A Comprehensive Protection Method for Securing the Organization\u2019s Network against Cyberattacks. Proceedings of the 2019 Cybersecurity and Cyberforensics Conference (CCC), Melbourne, VIC, Australia.","DOI":"10.1109\/CCC.2019.00005"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sudar, K., Deepalakshmi, P., Nagaraj, P., and Muneeswaran, V. (2020, January 26\u201327). Analysis of Cyberattacks and its Detection Mechanisms. Proceedings of the 2020 5th International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), Bangalore, India.","DOI":"10.1109\/ICRCICN50933.2020.9296178"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Housh, M., Kadosh, N., and Haddad, J. (2022). Detecting and Localizing Cyber-Physical Attacks in Water Distribution Systems without Records of Labeled Attacks. Sensors, 22.","DOI":"10.3390\/s22166035"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gaggero, G.B., Caviglia, R., Armellin, A., Rossi, M., Girdinio, P., and Marchese, M. (2022). Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm. Sensors, 22.","DOI":"10.3390\/s22103933"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yin, X.C., Liu, Z.G., Nkenyereye, L., and Ndibanje, B. (2019). Toward an Applied Cyber Security Solution in IoT-Based Smart Grids: An Intrusion Detection System Approach. Sensors, 19.","DOI":"10.3390\/s19224952"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Villalba, D.A.M., Varon, D.F.M., P\u00f3rtela, F.G., and Triana, O.A.D. (2022, January 14\u201316). Intrusion Detection System (IDS) with anomaly-based detection and deep learning application. Proceedings of the 2022 V Congreso Internacional en Inteligencia Ambiental, Ingenier\u00eda de Software y Salud Electr\u00f3nica y M\u00f3vil (AmITIC), San Jose, Costa Rica.","DOI":"10.1109\/AmITIC55733.2022.9941277"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"821","DOI":"10.26599\/TST.2020.9010041","article-title":"Anomaly detection of industrial control systems based on transfer learning","volume":"26","author":"Wang","year":"2021","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lysenko, S., Bobrovnikova, K., Shchuka, R., and Savenko, O. (2020, January 14\u201318). A Cyberattacks Detection Technique Based on Evolutionary Algorithms. Proceedings of the 2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT), Kyiv, Ukraine.","DOI":"10.1109\/DESSERT50317.2020.9125016"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Naik Sapavath, N., Muhati, E., and Rawat, D.B. (2021, January 26\u201328). Prediction and Detection of Cyberattacks using AI Model in Virtualized Wireless Networks. Proceedings of the 2021 8th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)\/2021 7th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), Washington, DC, USA.","DOI":"10.1109\/CSCloud-EdgeCom52276.2021.00027"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tang, Z., Jiao, J., Zhang, P., Yue, M., Chen, C., and Yan, J. (2019, January 4\u20138). Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning. Proceedings of the 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA.","DOI":"10.1109\/PESGM40551.2019.8974076"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kao, J.B., and Jiang, J.R. (2019, January 3\u20136). Anomaly Detection for Univariate Time Series with Statistics and Deep Learning. Proceedings of the 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan.","DOI":"10.1109\/ECICE47484.2019.8942727"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TSP.2013.2294594","article-title":"Locality Statistics for Anomaly Detection in Time Series of Graphs","volume":"62","author":"Wang","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Heymann, S., Latapy, M., and Magnien, C. (2012, January 26\u201329). Outskewer: Using Skewness to Spot Outliers in Samples and Time Series. Proceedings of the 2012 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Istanbul, Turkey.","DOI":"10.1109\/ASONAM.2012.91"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, P., Zhang, J., Luo, S., Song, Y., Zhang, J., and Wang, Y. (2024). A Fusion Adaptive Cubature Kalman Filter Approach for False Data Injection Attack Detection of DC Microgrids. Electronics, 13.","DOI":"10.3390\/electronics13091612"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"S\u00f6\u011f\u00fct, E., and Erdem, O.A. (2023). A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems. Appl. Sci., 13.","DOI":"10.3390\/app13105993"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Siva Kumar, C., Kolla, H., Sravya, B., Sri, D.L., and Nikitha, G. (2023, January 23\u201325). Obtrusion Unmasking of Machine Learning-Based Analysis of Imbalanced Network Traffic. Proceedings of the 2023 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India.","DOI":"10.1109\/ICCCI56745.2023.10128335"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, H., and Lang, B. (2019). Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey. Appl. Sci., 9.","DOI":"10.3390\/app9204396"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"527","DOI":"10.3390\/jcp2030027","article-title":"Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning\u2014A Review","volume":"2","author":"Ahsan","year":"2022","journal-title":"J. Cybersecur. Priv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Govea, J., Gaibor-Naranjo, W., and Villegas-Ch, W. (2024). Transforming Cybersecurity into Critical Energy Infrastructure: A Study on the Effectiveness of Artificial Intelligence. Systems, 12.","DOI":"10.3390\/systems12050165"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chae, Y., Katenka, N., and DiPippo, L. (2019, January 26\u201328). An Adaptive Threshold Method for Anomaly-based Intrusion Detection Systems. Proceedings of the 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA.","DOI":"10.1109\/NCA.2019.8935045"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"49587","DOI":"10.1109\/ACCESS.2023.3277250","article-title":"Wojtulewicz, A. GAN Neural Networks Architectures for Testing Process Control Industrial Network Against Cyber-Attacks","volume":"11","author":"Zarzycki","year":"2023","journal-title":"IEEE Access."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zarzycki, K., Chaber, P., Cabaj, K., \u0141awry\u0144czuk, M., Marusak, P., Nebeluk, R., Plamowski, S., and Wojtulewicz, A. (2023). Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study. Sensors, 23.","DOI":"10.3390\/s23156778"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3860\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T13:24:36Z","timestamp":1718371476000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,14]]},"references-count":30,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123860"],"URL":"https:\/\/doi.org\/10.3390\/s24123860","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,14]]}}}