Distributed denial-of-service attack detection for smart grid wide area measurement system: A hybrid machine learning technique

Habib, Arslan; Hasan, Mohammad Kamrul; Hassan, Rosilah; Islam, Shayla; Thakkar, Rudrik; Vo, Nguyen Qui Tu

doi:10.1016/j.egyr.2023.05.087

Cited by 11 publications

(2 citation statements)

References 12 publications

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“…Since the communication network is of extreme significance in the PV system, DDoS attacks have a very disruptive impact on the system. Equations ( 6) and ( 7) present the original control signal and delayed signal, respectively, if the communication delay in the network produced by a DDoS attack is N 0 [37].…”

Section: Distributed Denial Of Service (Ddos) Attack Modelmentioning

confidence: 99%

Nonlinear Controller-Based Mitigation of Adverse Effects of Cyber-Attacks on the DC Microgrid System

Ali,

Akhter

2024

Electronics

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Cyber-attacks have adverse impacts on DC microgrid systems. Existing literature shows plenty of attack detection methods but lacks appropriate mitigation and prevention approaches for cyber-attacks in DC microgrids. To overcome this limitation, this paper proposes a novel solution based on a nonlinear controller to mitigate the adverse effects of various cyber-attacks, such as distributed denial of service attacks and false data injection attacks, on various components of a DC microgrid system consisting of a photovoltaic power source, a permanent magnet synchronous generator-based variable speed wind generator, a fuel cell, battery energy storage, and loads. To demonstrate the effectiveness of the proposed solution, single and repetitive cyber-attacks on specific components of the microgrid have been considered. An index-based quantitative improvement analysis for the proposed control method has been made. Extensive simulations have been performed by the MATLAB/Simulink V9 software. Simulation results demonstrate the effectiveness of the proposed nonlinear controller-based method in mitigating the adverse effects of cyber-attacks. Moreover, the performance of the proposed method is better than that of the proportional-integral controller. Due to the simplicity of the proposed solution, it can easily be implemented in real practice.

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Section: Distributed Denial Of Service (Ddos) Attack Modelmentioning

confidence: 99%

Nonlinear Controller-Based Mitigation of Adverse Effects of Cyber-Attacks on the DC Microgrid System

Ali,

Akhter

2024

Electronics

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“…Smart grids (SGs) are an evolving technology, which provides intelligent monitoring, inters connectivity of multiple modes of generation, two-way data transmission, and improved resource utilization [1]. By raising the number of connected devices, it is tedious for the SG to access the distributed network.…”

Section: Introductionmentioning

confidence: 99%

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment

Almuqren,

Aljameel,

Alqahtani

et al. 2023

Sensors

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Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

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A Wireless Emergency Alerts System for Warning Disasters by Using Distributed Databases, GPS and Machine Learning Enabled API Services

Al Mamun,

Shagar,

Raisa

et al. 2024

Studies in Computational Intelligence

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Distributed denial-of-service attack detection for smart grid wide area measurement system: A hybrid machine learning technique

Cited by 11 publications

References 12 publications

Nonlinear Controller-Based Mitigation of Adverse Effects of Cyber-Attacks on the DC Microgrid System

Nonlinear Controller-Based Mitigation of Adverse Effects of Cyber-Attacks on the DC Microgrid System

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment

A Wireless Emergency Alerts System for Warning Disasters by Using Distributed Databases, GPS and Machine Learning Enabled API Services

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