Proposed algorithm for smart grid DDoS detection based on deep learning

Diaba, Sayawu Yakubu; Elmusrati, Mohammed

doi:10.1016/j.neunet.2022.12.011

Cited by 34 publications

(15 citation statements)

References 63 publications

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“…Also, it can be observed that the WSEO-HDLCS algorithm extends greater ROC values on two classes. Finally, the improved performance of the WSEO-HDLCS technique can be ensured by studying the comparisons in Table 3 and Figure 11 [12,[22][23][24]. The simulation values portrayed that the hybrid deep belief and network GRU-recommended models have shown poor performance.…”

Section: Results Analysismentioning

confidence: 99%

“…Continuous wavelet transform (CWT) has been employed in the proposed method to transform 1D traffic data to a 2D time-frequency domain scalogram as the input to a wavelet CNN (WavCovNet) for detecting anomalous performance, with information by differentiating attack features in standard outlines. Diaba and Elmusrati [12] suggested a hybrid DL approach, which focused on DDoS attacks on the transmission framework of SGs. The recommended technique is hybridized by the GRU and CNN methods.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Results Analysismentioning

confidence: 99%

Section: Related Workmentioning

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

View full text Add to dashboard Cite

show abstract

“…In 2023, Mukherjee et al [2] have demonstrated an efficient formulation technique for anomaly detection in smart grids. Here, the suggested technique was highly efficient, robust and non-linear to identify the accurate locations of the intrusions for the detection of blind attacks in real-world applications.…”

Section: A Related Workmentioning

confidence: 99%

“…In 2023, Diaba et.al [2], [3] have proposed a hybridized deep learning-based methods to recognize the circulated Denial of Service (DOS) attacks in a smart grid over a communication structure. Here, the suggested technique was fused using GRU and Conventional Neural Networks (CNN).…”

Section: A Related Workmentioning

confidence: 99%

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Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN

Scholar,

(Senior)

2024

Preprint

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The integration of Information and Communication Technologies (ICT) into the conventional power grid defines a smart grid, overseeing electrical power distribution, generation, and utilization. Despite its benefits, the smart grid encounters communication challenges due to various abnormalities. Detecting these anomalies is crucial for identifying power outages, energy theft, equipment failure, structural faults, power consumption irregularities, and cyber-attacks. While power systems adeptly handle natural disturbances, discerning cyber-attack-induced anomalies proves complex. This paper introduces an intelligent deep learning approach for smart grid anomaly detection. Initially, data is collected from standard smart meter, weather, and user behavior sources. Optimal weighted feature selection, utilizing the Modified Flow Direction Algorithm (MFDA), precedes inputting selected features into the "Adaptive Residual Recurrent Neural Network with Dilated Gated Recurrent Unit (ARRNN-DGRU)" for anomaly identification. Simulation results affirm the model's superior performance, with a heightened detection rate compared to existing methods, bolstering the smart grid system's robustness.

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Intrusion Detection Using Time-Series Imaging and Transfer Learning in Smart Grid Environments

Naaj,

Himeur,

Mansoor

et al. 2024

Lecture Notes in Networks and Systems

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Proposed algorithm for smart grid DDoS detection based on deep learning

Cited by 34 publications

References 63 publications

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

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

Optimizing Anomaly Detection in Smart Grids with Modified FDA and Dilated GRU-based Adaptive Residual RNN

Intrusion Detection Using Time-Series Imaging and Transfer Learning in Smart Grid Environments

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