AI-Based Malicious Encrypted Traffic Detection in 5G Data Collection and Secure Sharing DOI Open Access
Gang Han, Hailin Zhang, Zhong-Liang Zhang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 14(1), P. 51 - 51

Published: Dec. 26, 2024

With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex security environment more diverse attack methods. Unlike traditional networks, networks feature higher connection density, faster data transmission speeds, lower latency, which widely applied scenarios such as smart cities, Internet Things, autonomous driving. The vast amounts sensitive generated these applications become primary targets during processes collection secure sharing, unauthorized access or tampering could lead to severe breaches integrity issues. However, extensively employ encryption protect transmission, attackers can hide malicious content within encrypted communication, rendering content-based traffic detection methods ineffective for identifying traffic. To address this challenge, paper proposes a method based on reconstructive domain adaptation adversarial hybrid neural networks. proposed integrates generative with ResNet, ResNeXt, DenseNet construct network, aiming tackle challenges detection. On basis, module is introduced reduce distribution discrepancy between source target domain, thereby enhancing cross-domain capabilities. By preprocessing from public datasets, capable extracting deep features without need decryption. generator utilizes generate realistic samples, while discriminator achieves sample classification through high-dimensional extraction. Additionally, classifier further improves model’s stability generalization across different environments time periods. Experimental results demonstrate that significantly accuracy efficiency environments, effectively performance

Language: Английский

AI-Based Malicious Encrypted Traffic Detection in 5G Data Collection and Secure Sharing DOI Open Access
Gang Han, Hailin Zhang, Zhong-Liang Zhang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 14(1), P. 51 - 51

Published: Dec. 26, 2024

With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex security environment more diverse attack methods. Unlike traditional networks, networks feature higher connection density, faster data transmission speeds, lower latency, which widely applied scenarios such as smart cities, Internet Things, autonomous driving. The vast amounts sensitive generated these applications become primary targets during processes collection secure sharing, unauthorized access or tampering could lead to severe breaches integrity issues. However, extensively employ encryption protect transmission, attackers can hide malicious content within encrypted communication, rendering content-based traffic detection methods ineffective for identifying traffic. To address this challenge, paper proposes a method based on reconstructive domain adaptation adversarial hybrid neural networks. proposed integrates generative with ResNet, ResNeXt, DenseNet construct network, aiming tackle challenges detection. On basis, module is introduced reduce distribution discrepancy between source target domain, thereby enhancing cross-domain capabilities. By preprocessing from public datasets, capable extracting deep features without need decryption. generator utilizes generate realistic samples, while discriminator achieves sample classification through high-dimensional extraction. Additionally, classifier further improves model’s stability generalization across different environments time periods. Experimental results demonstrate that significantly accuracy efficiency environments, effectively performance

Language: Английский

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