Mr. Xuesong Guo Profile

Xuesong Guo

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Proceedings Article | 26 May 2023 Paper

HKIT-Trans: Huber-KNN and improved tab-transformer model for network security situation prediction

Zihan Xiong, Xuesong Guo, Jun Chen, Dabei Chen, Yuan Feng

Proceedings Volume 12700, 1270036 (2023) https://doi.org/10.1117/12.2682299

KEYWORDS: Network security, Computer security, Transformers, Data modeling, Feature extraction, Neural networks, Information security, Education and training, Machine learning, Head

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With the rise of new threats represented by advanced Persistent Threat Attack (APT), traditional network security methods such as vulnerability scanning, and intrusion detection can no longer meet the actual needs. Therefore, it is increasingly necessary to make full use of more security data for Network Security Situation Prediction (NSSP) to ensure the safe operation of Internet services. In this paper, the HKIT-Trans model is effectively proposed for the NSSP task. The aim of this work is to predict the current network condition by exploiting network data characteristics. The original network data has a large number of missing null values, and it is difficult to obtain data characteristics. Therefore, we design a null filling method combining Huber regression and KNN algorithm to improve the availability of data. At the same time, we propose the improved tab-transformer algorithm to extract network data features and perform inference. The experimental results show that the proposed method has better classification performance considering the existing techniques. Compared with the same baseline model, the AUC value of our proposed null filling method is increased by 1.94%-14.91%. For the classification effect of dataset CNCERT, HKIT-Trans is 7.14%higher than Huber-KNN with Logistic Regression, and 2.26%higher than Huber-KNN with LDA.

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