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

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

doi:10.1117/12.2682299

26 May 2023 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, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 1270036 (2023) https://doi.org/10.1117/12.2682299
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

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.

Citation Download Citation

Zihan Xiong, Xuesong Guo, Jun Chen, Dabei Chen, and Yuan Feng "HKIT-Trans: Huber-KNN and improved tab-transformer model for network security situation prediction", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 1270036 (26 May 2023); https://doi.org/10.1117/12.2682299

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KEYWORDS

Network security

Computer security

Data modeling

Transformers

Feature extraction

Neural networks

Education and training

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