A novel network security situation assessment technology for zero-day attacks based on improved adversarial autoencoder

Runjie Liu; Yiyang Liu; Le Chen

doi:10.1117/12.3011827

7 December 2023 A novel network security situation assessment technology for zero-day attacks based on improved adversarial autoencoder

Runjie Liu, Yiyang Liu, Le Chen

Author Affiliations +

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294153 (2023) https://doi.org/10.1117/12.3011827
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

In response to the difficulty in detecting and evaluating Zero-day Attack in the field of network security, this paper proposes a novel network security situation assessment technology based on deep learning. This research introduces a two-phase assessment model to achieve the detection and assessment of unknown attacks. The first phase focuses on reconstruction-based network situation anomaly detection, which is utilized to detect and assess anomalous traffic, including unknown attacks. In the second phase, a network attack identification system is developed to identify various types of known attacks. The overall security situation value is quantified by applying a weighted average to the results obtained from both phases. The method was validated on the public benchmark dataset UNSW-NB15, and the experimental results showed that the proposed technique has the ability to evaluate unknown Zero-day attacks, and the evaluation of known attacks is better than the baseline and existing models. By leveraging this technology, network security managers can gain a comprehensive understanding of the current threat landscape faced by the network. This empowers them to actively defend the network security system, mitigate the risk of unknown network attacks to system resources, and ensure the overall security of the network system.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Runjie Liu, Yiyang Liu, and Le Chen "A novel network security situation assessment technology for zero-day attacks based on improved adversarial autoencoder", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294153 (7 December 2023); https://doi.org/10.1117/12.3011827

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KEYWORDS

Network security

Data modeling

Education and training

Security technologies

Feature extraction

Machine learning

Network architectures

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