Denial-of-Service Attacks Pre-Emptive and Detection Framework for Synchrophasor Based Wide Area Protection Applications

Chawla, Astha; Singh, Animesh; Agrawal, Prakhar; Panigrahi, Bijaya Ketan; Bhalja, Bhavesh R.; Paul, Kolin

doi:10.1109/jsyst.2021.3093494

Cited by 14 publications

(6 citation statements)

References 35 publications

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“…So, a robust Anomaly-based IDS is required to detect anomalies with higher accuracy, low false negatives (missing rate), and low false positives (false alarms) with decreased response times. In the mission-critical infrastructure of a smart grid, even a few instances of misclassification might have fatal consequences regarding the power system's stability and reliability, which necessitates thorough investigation [21]. Our proposed SAML-Triple approach is an alternative solution to the existing Specification-based IDS (state estimation approach) and Signature-based IDS.…”

Section: Introductionmentioning

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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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: Introductionmentioning

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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“…developed a detection and isolation method against Dos by considering the attack impact on the communication channels. In [13], the attack feature of Dos on the power system is analyzed. Based on the invasion pathway of the FDIA, Cao et al.…”

Section: Introductionmentioning

confidence: 99%

“…The current security research on FDIA can be mainly divided into two aspects, such as detection-based and defense-based safety mechanism [9]. In the detectionbased safety mechanism, scholars have developed different attack characteristics-based attack detection algorithms [10][11][12][13][14][15]. Considering several cases of severe data-injections with high probabilities of information loss, a Bayesian-based approximated filter (BAF) was proposed to improve the immunity of the monitoring applications against data-injection attacks [10].…”

Section: Introductionmentioning

confidence: 99%

“…In [12], Valencia et al developed a detection and isolation method against Dos by considering the attack impact on the communication channels. In [13], the attack feature of Dos on the power system is analyzed. Based on the invasion pathway of the FDIA, Cao et al proposed a focal-loss-lightgbm ensemble classifier to detect the injected FDIA in cyber-physical system [14].…”

Section: Introductionmentioning

confidence: 99%

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A novel detection and defense mechanism against false data injection attack in smart grids

Cui

Gao

Guo

2023

IET Generation Trans & Dist

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As the next generation of green power system, smart grids have gradually enhanced the operation efficiency of power system. Meanwhile, the application of communication and intelligent technologies make the power grid more vulnerable to the emerging cyber‐physical attacks, such as the false data injection attack (FDIA). Particularly, the deception property of the FDIA on the output measurement estimation can fool the current security mechanism without triggering an alarm. Motivated by this problem, this paper aims at developing a novel detection and recovery mechanism against FDIA in smart grid. Based on the established state space grid model derived from the three‐phase sinusoidal voltage equations, an improved principal component analysis (PCA)‐based detection method is proposed. By introducing the mathematical transformation principle method, the detection performance such as detection rate and false positive rate can be improved. To keep the stable running of power system, a genetic optimization algorithm‐based linear quadratic regulator (LQR) defense method is developed. In addition, to improve the response performance to external attacks, an artificial intelligence method named genetic optimization algorithm is introduced to optimize the robust performance of the proposed defense method. Finally, the simulation results on the IEEE 6‐bus and 118‐bus grid system demonstrate the superiority of the proposed genetic algorithm optimization‐based LQR defense method.

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“…The responses of animal species to habitat degradation, including habitat fragmentation and adaptation of small-scale animal populations to anthropogenic disturbance, as well as mechanisms of adaptation at a larger scale (e.g. climate change) will be studied [1,2]. Habitat loss and fragmentation is a cumulative process that changes over time.…”

Section: Introductionmentioning

confidence: 99%

Animal Comfort Studies in Natural Environment Reserves Integrating RS and GIS

2020

NEP

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Animals are an important part of the natural ecosystem, man's closest friend, an important source of wealth for human society, and a key factor in maintaining ecological balance and the harmonious development of human society. The composition of a fauna includes species composition, relative abundance and species diversity. In order to study the response of faunal communities to environmental change, it is necessary to first understand the species diversity of these communities, to observe the temporal dynamics of species composition and species richness, and to analyse the changing factors affecting the diversity of disturbed communities. The main objective of this paper is to develop a study of animal amenity in natural environmental reserves using RS and GIS techniques. In this paper, seven factors were selected to determine the influence of each natural factor and the parameters of the factors using the frequency of occurrence of animal trace points, to establish a suitable evaluation system using hierarchical analysis, and to evaluate the suitability of the protected areas before and after respectively. The impact on animal habitats is analysed by comparing the approximate distribution of damaged areas using classification. In this paper, the road factor evaluation of suitable areas for animals in nature reserves was graded and statistical analysis of the area was carried out to obtain statistics on the area of suitable areas for animals in nature reserves based on the road factor. The results show that 56.34% of the total area of the reserve is suitable for animals based on the road factor, 14.75% is sub-suitable and 28.91% is unsuitable.

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Denial-of-Service Attacks Pre-Emptive and Detection Framework for Synchrophasor Based Wide Area Protection Applications

Cited by 14 publications

References 35 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

A novel detection and defense mechanism against false data injection attack in smart grids

Animal Comfort Studies in Natural Environment Reserves Integrating RS and GIS

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