Title: Research on malicious traffic detection based on image recognition

Authors: Wei Li; Yuliang Chen; Lixin Zhao; Yazhou Luo; Xin Liu

Addresses: North China Branch of State Grid Corporation of China, Beijing, China ' School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China ' North China Branch of State Grid Corporation of China, Beijing, China ' North China Branch of State Grid Corporation of China, Beijing, China ' School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China

Abstract: With the rapid development of the internet, information security problems caused by malicious traffic are becoming more and more serious. Malicious traffic invades the target system, interferes with the regular operation of the target internet device, steals user privacy, and destroys network availability. Therefore, this paper proposes a malicious traffic detection method based on image recognition technology, which is used to detect network traffic data, mine malicious traffic, provide early warning for users, and avoid network security threats. Based on the feature extraction of the text information of the network traffic data, the method converts the string data of the network traffic into picture data containing feature information, and combines the convolutional neural network (CNN) to realise the analysis of the attack vector detection on network traffic. Experimental results show that, compared with traditional machine learning methods, this method has a more efficient and accurate identification ability for malicious traffic attacks.

Keywords: web attack; malicious traffic detection; image recognition; convolutional neural network; CNN.

DOI: 10.1504/IJES.2023.136387

International Journal of Embedded Systems, 2023 Vol.16 No.2, pp.134 - 142

Received: 17 Feb 2023
Accepted: 18 Jul 2023
Published online: 31 Jan 2024 *

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