Early detection of cyber–physical attacks on fast charging stations using machine learning considering vehicle-to-grid operation in microgrids

Warraich, Z.S.; Morsi, Walid G.

doi:10.1016/j.segan.2023.101027

Cited by 14 publications

(4 citation statements)

References 23 publications

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“…To enhance e ciency in anti-phishing techniques, [23] presents an improved predictive model based on machine learning, utilizing six different algorithms and [24] focuses on cyberattacks related to fast-charging stations and introduces a machine learning-based approach for early detection.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Model for Predicting Phishing Websites

Boussi,

Gupta,

Hossain

2023

Preprint

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There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes can occur anywhere in the world, as the location of both the victim and the criminal is not a limiting factor. Different countries may have different common types of cybercrime, influenced by factors such as the country's economic situation, level of internet activity, and overall development. Phishing is a prevalent type of cybercrime in the financial sector, regardless of the country's circumstances. While the phishing techniques used in developed countries may differ from those in developing countries, the impact remains the same, resulting in financial losses. In our work, a dataset consisting of 48 features extracted from 5,000 phishing webpages and 5,000 legitimate webpages was used to predict whether a website is phishing or not, achieving an accuracy of 98%.

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Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Model for Predicting Phishing Websites

Boussi,

Gupta,

Hossain

2023

Preprint

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“…The baseline settings of both frequencies and voltages are changed after 0.1 s, while RL-oriented methods keep the rest of the systems in check. To research the delayed advantage-actor dissent-oriented MAS RL technique described in 166 , a four-line rechargeable ESD simulation was carried out. When harmonic frequency management and SOC balance synchronization can be done jointly in the face of DoS attacks, the suggested system can attain its full potential as a system.…”

Section: Advance Hierarchical Controlmentioning

confidence: 99%

Review on microgrids design and monitoring approaches for sustainable green energy networks

Ahmed,

Rehan,

Basit

et al. 2023

Sci Rep

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Microgrids are power distribution systems that can operate either in a grid-connected configuration or in an islanded manner, depending on the availability of decentralized power resources, such as sustainable or non-sustainable power sources, battery backup systems, and power demands. The extensive adoption of inverter-based systems poses numerous technological challenges, necessitating a centralized management system to assure the system reliability and monitoring of the energy delivery networks. Thus, this research begins by highlighting these significant obstacles and then analyzes the present-day advances in multilevel control architecture for delivering on promised functionality. This article also discusses the development of innovative control technologies, such as introducing collaborative distributed approaches and reducing conventional three-stage patriarchal administration to fewer stages of system integration and functioning.

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“…The author [8] proposed an intrusion detection approach to early detect the cyber-physical attacks targeting Fast Charging Station (FCS) considering Vehicle-to-Grid (V2G) operation. In this discreate power samples data is used and use the Gini index for calculation of power mid-point and then use the DT for the detection of DoS attack.…”

Section: Literature Surveymentioning

confidence: 99%

To propose an attack detection model for enhancing the security of 5G-enabled Vehicle-to-Everything (V2X) communication for smart vehicle

Rajak,

Mishra

2023

Annals of Computer Science and Information Systems

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The evolution of 5G technology has revolutionized the communication landscape, enabling faster speeds, low latency, and increased capacity. The integration of 5G technology and the emergence of the 5G-enabled V2X communication network are driving the transformation of the automotive industry. Connected cars and the software-defined vehicle concept enable new business models and enhanced safety measures. However, ensuring the security of the 5G-enabled V2X communication network is crucial to mitigate potential attacks and protect the integrity of the ecosystem. To identify this potential attack, we have proposed a novel deep learning-based attack detection model (ADM) for detecting attack in 5G-enabled V2X communication network. In this we have used correlation coefficient as the feature selection method and used the deep learning-based stacked LSTM model for attack detection. The performance metrices are detection rate, accuracy, precision and F1-score.

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Early detection of cyber–physical attacks on fast charging stations using machine learning considering vehicle-to-grid operation in microgrids

Cited by 14 publications

References 23 publications

A Machine Learning Model for Predicting Phishing Websites

A Machine Learning Model for Predicting Phishing Websites

Review on microgrids design and monitoring approaches for sustainable green energy networks

To propose an attack detection model for enhancing the security of 5G-enabled Vehicle-to-Everything (V2X) communication for smart vehicle

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