Digital Watermark based Authentication for Intrusion Detection of Digital Substations

The paper proposes the use of digital watermark based authentication for intrusion detection in IEC 61850- automated substations. The watermark can be embedded into the Least Significant Bits of the measurements without visible deterioration in precision. When Intelligent Electronics Devices gets measurements, the watermark in the measurement can be retrieved to determine whether it has been attacked and detect malicious intrusion. The proposed approach is appropriate for the time critical and resource constrained applications in substation automation system for its simplicity. Numerical simulation shows that the process latency and error incurred by watermarking is acceptable and will not impact performance of protective function in IEC 61850 automated substations.

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A Survey on Vulnerabilities and Countermeasures in the Communications of the Smart Grid

Electronics ◽

10.3390/electronics10161881 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1881

Author(s):

Jesús Lázaro ◽

Armando Astarloa ◽

Mikel Rodríguez ◽

Unai Bidarte ◽

Jaime Jiménez

Keyword(s):

Smart Grid ◽

Real Time ◽

International Electrotechnical Commission ◽

Communication Infrastructure ◽

Iec 61850 ◽

Best Effort ◽

Electrical Grid ◽

Informational Technology ◽

Iec 62351 ◽

Time Critical

Since the 1990s, the digitalization process has transformed the communication infrastructure within the electrical grid: proprietary infrastructures and protocols have been replaced by the IEC 61850 approach, which realizes interoperability among vendors. Furthermore, the latest networking solutions merge operational technologies (OTs) and informational technology (IT) traffics in the same media, such as time-sensitive networking (TSN)—standard, interoperable, deterministic, and Ethernet-based. It merges OT and IT worlds by defining three basic traffic types: scheduled, best-effort, and reserved traffic. However, TSN demands security against potential new cyberattacks, primarily, to protect real-time critical messages. Consequently, security in the smart grid has turned into a hot topic under regulation, standardization, and business. This survey collects vulnerabilities of the communication in the smart grid and reveals security mechanisms introduced by international electrotechnical commission (IEC) 62351-6 and how to apply them to time-sensitive networking.

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Detecting cybersecurity attacks across different network features and learners

Journal Of Big Data ◽

10.1186/s40537-021-00426-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Joffrey L. Leevy ◽

John Hancock ◽

Richard Zuech ◽

Taghi M. Khoshgoftaar

Keyword(s):

Feature Selection ◽

Intrusion Detection ◽

Operating Characteristic ◽

Characteristic Curve ◽

Machine Learning Algorithms ◽

Feature Selection Technique ◽

Impact Performance ◽

Detection Model ◽

Wide Range ◽

Research Questions

AbstractMachine learning algorithms efficiently trained on intrusion detection datasets can detect network traffic capable of jeopardizing an information system. In this study, we use the CSE-CIC-IDS2018 dataset to investigate ensemble feature selection on the performance of seven classifiers. CSE-CIC-IDS2018 is big data (about 16,000,000 instances), publicly available, modern, and covers a wide range of realistic attack types. Our contribution is centered around answers to three research questions. The first question is, “Does feature selection impact performance of classifiers in terms of Area Under the Receiver Operating Characteristic Curve (AUC) and F1-score?” The second question is, “Does including the Destination_Port categorical feature significantly impact performance of LightGBM and Catboost in terms of AUC and F1-score?” The third question is, “Does the choice of classifier: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Catboost, LightGBM, or XGBoost, significantly impact performance in terms of AUC and F1-score?” These research questions are all answered in the affirmative and provide valuable, practical information for the development of an efficient intrusion detection model. To the best of our knowledge, we are the first to use an ensemble feature selection technique with the CSE-CIC-IDS2018 dataset.

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