Detection of DDoS Attacks in the Internet of Things Based on Artificial Intelligence

doi:10.3969/j.issn.1671-1122.2024.11.001

Abstract

Abstract:

Aiming at the optimal solution for detecting IoT DDoS attacks, this paper used multiple algorithms to detect and model IoT DDoS attacks. This paper used kernel density estimation to screen out influential traffic feature fields. A DDoS attack detection model based on machine learning and deep learning algorithms was established. The feasibility of processing data sets and performing attack detection through reversible residual neural networks and large language models was analyzed. Experimental results show that the ResNet50 algorithm performs best in comprehensive indicators. In distinguishing DDoS attack traffic from other traffic issues, the gradient boosting algorithm performs better. In terms of segmenting DDoS attack types, the optimized ResNet50-GRU algorithm performs better.

Key words: IoT, DDoS attacks, machine learning, deep learning algorithms, residual neural network

CLC Number:

TP309

YIN Jie, CHEN Pu, YANG Guinian, XIE Wenwei, LIANG Guangjun. Detection of DDoS Attacks in the Internet of Things Based on Artificial Intelligence[J]. Netinfo Security, 2024, 24(11): 1615-1623.

Figures/Tables 12

References 21

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