earticle

논문검색

BM3D and Deep Image Prior based Denoising for the Defense against Adversarial Attacks on Malware Detection Networks

원문정보

초록

영어

Recently, Machine Learning-based visualization approaches have been proposed to combat the problem of malware detection. Unfortunately, these techniques are exposed to Adversarial examples. Adversarial examples are noises which can deceive the deep learning based malware detection network such that the malware becomes unrecognizable. To address the shortcomings of these approaches, we present Blockmatching and 3D filtering (BM3D) algorithm and deep image prior based denoising technique to defend against adversarial examples on visualization-based malware detection systems. The BM3D based denoising method eliminates most of the adversarial noise. After that the deep image prior based denoising removes the remaining subtle noise. Experimental results on the MS BIG malware dataset and benign samples show that the proposed denoising based defense recovers the performance of the adversarial attacked CNN model for malware detection to some extent.

목차

Abstract
1. Introduction
2. Related Works
3. Adversarial Attack on Malware detecting Convolutional Neural Network
3.1 Visualization
3.2 ML-based malware visualization detection approach
3.3 Generation of Adversarial Examples
4. Denoising based Adversarial Defense for Malware Detection Neural Network
5. Experiments
5.1 Dataset
5.2 Experimental Setup
5.3 Results and Discussion
6. Conclusion
Acknowledgement
References

저자정보

  • Kumi Sandra Researcher, Department of Computer Engineering, Dongseo University, Korea
  • Suk-Ho Lee Professor, Department of Computer Engineering, Dongseo University, Korea

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 기관로그인 시 무료 이용이 가능합니다.

      • 4,000원

      0개의 논문이 장바구니에 담겼습니다.