Intelligent Approach for Analysis and Diagnosis of Attack, Fault and Load Variation in SCADA Systems: A Power System Application

Mohan, Vamshi Sunku; Sankaran, Sriram

doi:10.1007/978-981-16-6081-8_1

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Sunku Mohan et al [30] investigated the problem using Power Domain Knowledge. These authors employed manual feature selection by filtering out features of positive-, negative-, and zero-sequence components and logs.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, the feature extraction using PCA [27] fails to capture the local structure or relationships within the data, which might result in misclassification between the three classes. Also, the author in [30] undertook manual feature selection, which may not be suitable for the generalizability and scalability of the model for different architectures and may require complex logical calculations. Meanwhile, our SAML-Triple (INFAZ) can be scalable and generalizable to the IEEE 'N' bus system for different architectures.…”

Section: Saml-triple: Triple-class Datasets (No Events/natural Events...mentioning

confidence: 99%

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Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

Murugesan,

Velu,

Palaniappan

et al. 2024

Energies

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In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Saml-triple: Triple-class Datasets (No Events/natural Events...mentioning

confidence: 99%

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

Murugesan,

Velu,

Palaniappan

et al. 2024

Energies

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show abstract

“…The drawback of this paper is that they removed the "INFinity" attack records, which may lead to missing rate (or) SCADA inoperable. Sunku Mohan et al [19] investigated the problem using Power Domain Knowledge. This author made a manual feature selection by ltering out features of positive, negative, and zero sequence components and logs.…”

Section: Related Workmentioning

confidence: 99%

Towards Early Cyberattack Discrimination in the Smart Grid Using Statistical Approach with Machine Learning Techniques

et al. 2023

Preprint

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Smart Grid has been exposed to cyberattacks that penetrate the Supervisory Control and Data Acquisition systems, causing privacy and access control violations on security infrastructures. It is critical to address this issue with an early attack detection mechanism with a reduced missing rate and less response time that discriminates cyberattacks against natural and man-made events. Developing a robust intrusion detection system is still a challenging research problem to prevent the smart grid system from sabotage and processing downtime. This paper aims to develop a robust Anomaly-Based Intrusion Detection System using the statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of Statistical Approach with a Machine Learning classifier (SAML) based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting respectively is proposed, to optimally tune the hyperparameters for early discrimination of Triple class events. The proposed model is tested on the publicly available Industrial Control Systems Cyber Attack Power System Triple Class Dataset. The test results provide a higher detection accuracy with a low missing rate, false alarm rate, and less response time than the existing approaches.

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Multi-Level Security Architecture for Edge Intelligent Devices with Modbus Tcp/Ip Communication Protocols

Hussain,

Krishnaveni

2024

SSRN Journal

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Intelligent Approach for Analysis and Diagnosis of Attack, Fault and Load Variation in SCADA Systems: A Power System Application

Cited by 4 publications

References 23 publications

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

Towards Early Cyberattack Discrimination in the Smart Grid Using Statistical Approach with Machine Learning Techniques

Multi-Level Security Architecture for Edge Intelligent Devices with Modbus Tcp/Ip Communication Protocols

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