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Anomaly Detection Dataset for Industrial Control Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE Res Inst, S-50115 Pitea, Sweden..ORCID iD: 0000-0001-5332-1033
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Malardalen Univ, Sch Innovat Design & Engn, S-72123 Vasteras, Sweden..ORCID iD: 0000-0003-3354-1463
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. RISE Res Inst, S-50115 Pitea, Sweden..ORCID iD: 0000-0002-7235-6888
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 107982-107996Article in journal (Refereed) Published
Abstract [en]

Over the past few decades, Industrial Control Systems (ICS) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. This paper introduces the 'ICS-Flow' dataset, which offers network data and process state variables logs for supervised and unsupervised ML-based IDS assessment. The network data includes normal and anomalous network packets and flows captured from simulated ICS components and emulated networks, where the anomalies were applied to the system through various cyberattacks. We also proposed an open-source tool, "ICSFlowGenerator," for generating network flow parameters from Raw network packets. The final dataset comprises over 25,000,000 raw network packets, network flow records, and process variable logs. The paper describes the methodology used to collect and label the dataset and provides a detailed data analysis. Finally, we implement several ML models, including the decision tree, random forest, and artificial neural network to detect anomalies and attacks, demonstrating that our dataset can be used effectively for training intrusion detection ML models.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2023. Vol. 11, p. 107982-107996
Keywords [en]
Anomaly detection dataset, industrial control system, intrusion detection, cyberattack, network flow, artificial intelligence
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-65227DOI: 10.1109/ACCESS.2023.3320928ISI: 001121774800001OAI: oai:DiVA.org:mdh-65227DiVA, id: diva2:1823927
Available from: 2024-01-03 Created: 2024-01-03 Last updated: 2024-01-03Bibliographically approved

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Dehlaghi Ghadim, AlirezaHelali Moghadam, MahshidBalador, AliHansson, Hans

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