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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.4
- 수록면
- 514 - 523 (10page)
- DOI
- 10.9717/kmms.2024.27.4.514
이용수
초록· 키워드
오류제보하기
With the rapid advancements in artificial intelligence and informatization, there has been an increase in the incidence of malicious code for Linux systems. In paper, we utilize a Convolutional Neural Networks (CNNs) to detect Linux malware, which is commonly found in Internet of Things (IoT) devices. To address the inherent ‘black-box’ nature of artificial intelligence, we employe Layer-wise Relevance Propagation (LRP), a technique within the scope of eXplainable Artificial Intelligence (XAI). This approach allowed us to visualize and identify key features that were highly influential in the CNN’s input image, offering insights into the basis of the CNN’s decision-making process. Our study aims to enhance understanding by visualizing between malware and normal files in Linux, processing them through CNNs, and then applying LRP for visualization. Our study achieved an impressive accuracy of 98.9%. The results of LRP provided a means to interpret which features are significant in the decision-making process of the CNN. Our findings provide insight into the future that XAI will increasingly be integrated into AI-based malware systems.
목차
ABSTRACT
1. 서론
2. 관련연구
3. 배경
4. 제안 방법
5. XAI 적용
6. 실험 결과
7. 결론
REFERENCE
참고문헌 (15)
참고문헌 신청
S.O. Jeong, T.H. Han, S.B. Lim, and T.J. Lee, “A Study on Effective Adversarial Attack Creation for Robustness Improvement of AI Models,” Journal of Internet Computing and Services (JICS), Vol. 24, No. 4, pp. 25-36, 2023.
S.O. Choi, Y.S. Kim, J.H. Kim, and I.K. Kim, “Research Trends in Malware Detection Using Deep Learning,” Korea Institute of Information Security and Cryptology (KIISC), Vol. 27, No. 3, pp. 20-26, 2017.
J.H. Hwang and T.J. Lee, “Study of Static Analysis and Ensemble-Based Linux Malware Classification,” Journal of the Korea Institute of Information Security & Cryptology, Vol. 29, No. 6, pp. 1327-1337, 2019.
T.G. Kim, H.T. Ji, and E.G. Im, “Malware Classification Using Machine Learning and Binary Visualization,” KIISE Transactions on Computing Practices, Vol. 24, No. 4, pp. 198-203, 2018.
J.K. Lee and M. Lee, “Malware Classification Method Using Malware Visualization and Transfer Learning,” Proceedings of the Korean Institute of Information and Communication Sciences Conference, pp. 555-556, 2021.
S.O. Choi, Y.S. Kim, J.H. Kim, and I.K. Kim, “Research Trends in Malware Detection Using Deep Learning,” Korea Institute of Information Security and Cryptology (KIISC), Vol. 27, No. 3, pp. 20-26, 2017.
J.H. Hwang and T.J. Lee, “Study of Static Analysis and Ensemble-Based Linux Malware Classification,” Journal of the Korea Institute of Information Security & Cryptology, Vol. 29, No. 6, pp. 1327-1337, 2019.
T.G. Kim, H.T. Ji, and E.G. Im, “Malware Classification Using Machine Learning and Binary Visualization,” KIISE Transactions on Computing Practices, Vol. 24, No. 4, pp. 198-203, 2018.
J.K. Lee and M. Lee, “Malware Classification Method Using Malware Visualization and Transfer Learning,” Proceedings of the Korean Institute of Information and Communication Sciences Conference, pp. 555-556, 2021.
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