Advanced Cyber-Physical Attack Classification with Extreme Gradient Boosting for Smart Transmission Grids

Hu, Chengming; Yan, Jun; Wang, Chun

doi:10.1109/pesgm40551.2019.8973679

Cited by 19 publications

(8 citation statements)

References 18 publications

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“…As a result, it is crucial for security systems to be up-to-date against scams, malware (malicious software), spam, and phishing attacks [3]. Although one solution can be focusing on detection [4] and classification [5] of the cyber attacks, it will not be sufficient enough considering the fact that these attacks happen globally [6]. There is an evident need for deeper understanding of cyber attacks in terms of spatial analysis [7].…”

Section: Introductionmentioning

confidence: 99%

The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study

et al. 2020

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In this study, we aimed to identify spatial clusters of countries with high rates of cyber attacks directed at other countries. The cyber attack dataset was obtained from Canadian Institute for Cybersecurity, with over 110,000 Uniform Resource Locators (URLs), which were classified into one of 5 categories: benign, phishing, malware, spam, or defacement. The disease surveillance software SaTScanTM was used to perform a spatial analysis of the country of origin for each cyber attack. It allowed the identification of spatial and space-time clusters of locations with unusually high counts or rates of cyber attacks. Number of internet users per country obtained from the 2016 CIA World Factbook was used as the population baseline for computing rates and Poisson analysis in SaTScanTM. The clusters were tested for significance with a Monte Carlo study within SaTScanTM, where any cluster with p < 0.05 was designated as a significant cyber attack cluster. Results using the rate of the different types of malicious URL cyber attacks are presented in this paper. This novel approach of studying cyber attacks from a spatial perspective provides an invaluable relative risk assessment for each type of cyber attack that originated from a particular country.

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Section: Introductionmentioning

confidence: 99%

The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study

et al. 2020

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“…It is also used on load forecasting [60], anomaly detection [61,62], and stability assessment [63]. Boosting is another ensemble method that builds a new model that attempts to correct the misclassification from the previous model and shows promising results in smart grid problems [64][65][66]. Stacking, which is an ensemble learning technique that combines the predictions of several classification or regression algorithms, is well-developed for load forecasting [67], anomaly detection [68], and cyberattack detection [69].…”

Section: Ensemble Methodsmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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“…If finds application in interfacing components for the software-defined next-generation smart grid in conjunction with network management centre. The boosting technique, on the other hand, is an ensemble method that develops a different model that tries to rectify the misclassification issues from the earlier model and demonstrated favourable results in smart grid challenges [102]- [104]. Stacking as an ensemble learning method that aggregate the forecasts of various regression or labelling algorithms, is sophisticated for cyber-attack detection [105], load prediction [106], anomaly detection [107].…”

Section: ) Ensemble Technique In Sgmentioning

confidence: 99%

“…The result demonstrates a higher performance of the proposed model over other clustering methods. To addressing the laborious technique of developing an optimum DNN, that defines the number of hidden layers in the DNN model, [102] applied an ensemble technique that integrates various DNN algorithms with various numbers of hidden layers to attain total improved performance by removing the badly achieved models. Nevertheless, the processing overhead is a constraint, since several CNNs are involved.…”

Section: A: Short-term Load Forecastingmentioning

confidence: 99%

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

2022

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Smartgrid is a paradigm that was introduced into the conventional electricity network to enhance the way generation, transmission, and distribution networks interrelate. It involves the use of Information and Communication Technology (ICT) and other solution in fault and intrusion detection, mere monitoring of energy generation, transmission, and distribution. However, on one hand, the actual and earlier smartgrid, do not integrate more advanced features such as automatic decision making, security, scalability, self-healing and awareness, real-time monitoring, cross-layer compatibility, etc. On the other hand, the emergence of the digitalization of the communication infrastructure to support the economic sector which among them are energy generation and distribution grid with Artificial Intelligence (AI) and largescale Machine to Machine (M2M) communication. With the future Massive Internet of Things (MIoT) as one of the pillars of 5G/6G network factory, it is the enabler to support the next generation smart grid by providing the needed platform that integrates, in addition to the communication infrastructure, the AI and IoT support, providing a multitenant system. This paper aim at presenting a comprehensive review of next smart grid research trends and technological background, discuss a futuristic next-generation smart grid driven by artificial intelligence (AI) and leverage by IoT and 5G. In addition, it discusses the challenges of nextgeneration smart-grids as it relate to the integration of AI, IoT and 5G for better smart grid architecture. Also, proffers possible solutions to some of the challenges and standards to support this novel trend. A corresponding future work will dwell on the implementation of the discussed integration of AI, IoT and 5G for next-generation smart grid, using Matlab, NS2/NS3, Open-daylight and Mininet as soft tools and compare with related literature.

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Advanced Cyber-Physical Attack Classification with Extreme Gradient Boosting for Smart Transmission Grids

Cited by 19 publications

References 18 publications

The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study

The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study

Artificial Intelligence Techniques in Smart Grid: A Survey

Integrating Artificial Intelligence Internet of Things and 5G for Next-Generation Smartgrid: A Survey of Trends Challenges and Prospect

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