Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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A Data Sampling Technique for Secure Dataset Using Weight VAE Oversampling(W-VAE)

가중치 VAE 오버샘플링(W-VAE)을 이용한 보안데이터셋 샘플링 기법 연구

  • Kang, Hanbada (Department of Convergence Security, Chung-Ang University) ;
  • Lee, Jaewoo (Department of Industrial Security, Chung-Ang University)
  • Received : 2022.10.20
  • Accepted : 2022.11.09
  • Published : 2022.12.31
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Abstract

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

최근 인공지능 기술이 발전하면서 해킹 공격을 탐지하기 위해 인공지능을 이용하려는 연구가 활발히 진행되고 있다. 하지만, 인공지능 모델 개발에 핵심인 학습데이터를 구성하는데 있어서 보안데이터가 대표적인 불균형 데이터라는 점이 큰 장애물로 인식되고 있다. 이에 본 눈문에서는 오버샘플링을 위한 데이터 추출에 딥러닝 생성 모델인 VAE를 적용하고 K-NN을 이용한 가중치 계산을 통해 클래스별 오버샘플링 개수를 설정하여 샘플링을 하는 W-VAE 오버샘플링 기법을 제안한다. 본 논문에서는 공개 네트워크 보안 데이터셋인 NSL-KDD를 통해 ROS, SMOTE, ADASYN 등 총 5가지 오버샘플링 기법을 적용하였으며 본 논문에서 제안한 오버샘플링 기법이 F1-Score 평가지표를 통해 기존 오버샘플링 기법과 비교하여 가장 효과적인 샘플링 기법임을 증명하였다.

Keywords

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