An Improved DDoS Attack Detection Model Based on Unsupervised Learning in Smart Grid

Ma, Zhili; Ma, Hongzhong; Gao, Xiang; Gai, Jiyang; Zhang, Xuejun; He, Fucun; Zhao, Jinxiong

doi:10.1007/978-3-030-89814-4_40

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Ma et al [16] recommended an innovative DDoS attack identification technique that only applies unlabeled abnormal network traffic information to make the recognition system. This approach primarily utilizes the Balanced Iterative Reducing and Clustering utilizing the Hierarchies technique (BIRCH) for pre-clustering the anomalous network traffic data and, after examining AE, to make the identification method in unsupervised learning depends on clustering subsets.…”

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

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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: Related Workmentioning

confidence: 99%