{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:36:26Z","timestamp":1778603786080,"version":"3.51.4"},"reference-count":16,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Khalid University","award":["RGP.1\/147\/43"],"award-info":[{"award-number":["RGP.1\/147\/43"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Integrating IoT devices in SCADA systems has provided efficient and improved data collection and transmission technologies. This enhancement comes with significant security challenges, exposing traditionally isolated systems to the public internet. Effective and highly reliable security devices, such as intrusion detection system (IDSs) and intrusion prevention systems (IPS), are critical. Countless studies used deep learning algorithms to design an efficient IDS; however, the fundamental issue of imbalanced datasets was not fully addressed. In our research, we examined the impact of data imbalance on developing an effective SCADA-based IDS. To investigate the impact of various data balancing techniques, we chose two unbalanced datasets, the Morris power dataset, and CICIDS2017 dataset, including random sampling, one-sided selection (OSS), near-miss, SMOTE, and ADASYN. For binary classification, convolutional neural networks were coupled with long short-term memory (CNN-LSTM). The system\u2019s effectiveness was determined by the confusion matrix, which includes evaluation metrics, such as accuracy, precision, detection rate, and F1-score. Four experiments on the two datasets demonstrate the impact of the data imbalance. This research aims to help security researchers in understanding imbalanced datasets and their impact on DL SCADA-IDS.<\/jats:p>","DOI":"10.3390\/s23020758","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T07:55:39Z","timestamp":1673250939000},"page":"758","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["The Effect of Dataset Imbalance on the Performance of SCADA Intrusion Detection Systems"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5584-9042","authenticated-orcid":false,"given":"Asaad","family":"Balla","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2263-0850","authenticated-orcid":false,"given":"Mohamed Hadi","family":"Habaebi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9556-5861","authenticated-orcid":false,"given":"Elfatih A. A.","family":"Elsheikh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia"}]},{"given":"Md. Rafiqul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9771-1559","authenticated-orcid":false,"given":"F. M.","family":"Suliman","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.asoc.2018.06.017","article-title":"Securing the operations in SCADA-IoT platform based industrial control system using ensemble of deep belief networks","volume":"71","author":"Huda","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_2","first-page":"132","article-title":"Toward Constructing a Balanced Intrusion Detection Dataset Based on CICIDS2017","volume":"2","author":"Abdulrahman","year":"2020","journal-title":"Samarra J. Pure Appl. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wotawa, F., and Muhlburger, H. (2021, January 6\u201310). On the Effects of Data Sampling for Deep Learning on Highly Imbalanced Data from SCADA Power Grid Substation Networks for Intrusion Detection. Proceedings of the IEEE International Conference on Software Quality, Reliability and Security (QRS), Haikou, China.","DOI":"10.1109\/QRS54544.2021.00095"},{"key":"ref_4","unstructured":"Fundin, A. (2021). Generating Datasets Through the Introduction of an Attack Agent in a SCADA Testbed. [Master\u2019s Thesis, Link\u00f6ping University]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3379499","article-title":"A Taxonomy of Supervised Learning for IDSs in SCADA Environments","volume":"53","author":"Suaboot","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_6","unstructured":"Batarseh, F.A., and Yang, R. (2020). Foundations of data imbalance and solutions for a data democracy. Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering, Elsevier. [1st ed.]."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Peterson, J.M., Leevy, J.L., and Khoshgoftaar, T.M. (2021, January 23\u201326). A Review and Analysis of the Bot-IoT Dataset. Proceedings of the 15th IEEE International Conference on Service-Oriented System Engineering, SOSE 2021, Oxford, UK.","DOI":"10.1109\/SOSE52839.2021.00007"},{"key":"ref_8","unstructured":"(2022, November 25). Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning. Available online: http:\/\/www.cse.wustl.edu\/~jain\/papers\/ftp\/imb_isi.pdf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"32464","DOI":"10.1109\/ACCESS.2020.2973730","article-title":"Network Intrusion Detection Combined Hybrid Sampling with Deep Hierarchical Network","volume":"8","author":"Jiang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Miah, O., Khan, S., Shatabda, S., and Dewan, M.F. (2019, January 3\u20135). Improving Detection Accuracy for Imbalanced Network Intrusion Classification using Cluster-based Under-sampling with Random Forests. Proceedings of the International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh.","DOI":"10.1109\/ICASERT.2019.8934495"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ins.2018.10.029","article-title":"Under-sampling class imbalanced datasets by combining clustering analysis and instance selection","volume":"477","author":"Tsai","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_12","first-page":"1342","article-title":"Clustering Under-Sampling Data for Improving the Performance of Intrusion Detection System","volume":"16","author":"Aziz","year":"2021","journal-title":"JESTEC"},{"key":"ref_13","first-page":"1","article-title":"An efficient network intrusion detection approach based on deep learning","volume":"5","author":"Wang","year":"2021","journal-title":"Wirel. Netw."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ran, J., and Mi, J. (2019, January 19\u201320). An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic. Proceedings of the International Conference on Computer Science and Network Technology (ICCSNT), Dalian, China.","DOI":"10.1109\/ICCSNT47585.2019.8962490"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"50850","DOI":"10.1109\/ACCESS.2018.2868993","article-title":"A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks","volume":"6","author":"Wu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","unstructured":"Mobasheri, A. (2019). A Detailed Analysis of the CICIDS2017 Data Set, Springer. Communications in Computer and Information Science."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/758\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:04:37Z","timestamp":1760119477000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,9]]},"references-count":16,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020758"],"URL":"https:\/\/doi.org\/10.3390\/s23020758","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,9]]}}}