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A CNN-Based Encrypted Data Detection for Ransomware Defense
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랜섬웨어 방어를 위한 합성곱 신경망 기반의 데이터 암호화 탐지 기법

논문 기본 정보

Type
Academic journal
Author
Minji Kang (서울대학교) Jonghoon Won (서울대학교) Jisung Park (서울대학교) Jihong Kim (서울대학교)
Journal
Korean Institute of Information Scientists and Engineers KIISE Transactions on Computing Practices Vol.25 No.5 KCI Accredited Journals
Published
2019.5
Pages
279 - 283 (5page)
DOI
10.5626/KTCP.2019.25.5.279

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A CNN-Based Encrypted Data Detection for Ransomware Defense
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Research history (2)

2019

A CNN-Based Encrypted Data Detection for Ransomware Defense
Minji Kang ,  Jonghoon Won ,  Jisung Pa and others 2 KIISE Transactions on Computing Practices 2019.05 Academic journal

2018

A Deep Learning-Based Encrypted Data Detection Technique for Ransomware Defense
Minji Kang ,  Jonghoon Won ,  Jisung Pa and others 2 한국정보과학회 학술발표논문집 2018.06 Proceeding
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Abstract· Keywords

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With the rapid increase in the number of ransomwares recently, the development of real-time strategies for ransomware defense is imperative. To overcome the limitations of traditional ransomware defense techniques, a storage-level data recovery technique was suggested. However, as the technique inefficiently selects data to conserve, it has a negative impact on the lifetime and performance of storage. In this paper, we propose a CNN-based encrypted data detection technique to enhance the accuracy of selecting data to conserve while ensuring complete data recovery. Our experiments show that the proposed technique achieved 93.90% detection accuracy at the storage-level without any high-level information. Furthermore, by changing the loss function and controlling a detection threshold, we attained a false negative rate of nearly 0.
#랜섬웨어 #딥러닝 #합성곱 신경망 #컴퓨터 보안 #ransomware #deep learning #convolutional neural network #computer security

Contents

요약
Abstract
1. 서론
2. 합성곱 신경망 설계 및 부정 오류 감소 기법
3. 실험결과
4. 결론
References

References (5)

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A. Kharraz, S. Arshad, C. Mulliner, W. Robertson, and E. Kirda, UNVEIL: A Large-Scale, Automated Approach to Detecting Ransomware, Proc. of the 25th USENIX Security Symposium, pp. 757-772, 2016. google schola J. Huang, J. Xu, X. Xing, P. Liu, and M. K. Qureshi, FlashGuard: Leveraging Intrinsic Flash Properties to Defend Against Encryption Ransomware, Proc. of the 2017 ACM SIGSAC Conference, pp. 2231-2244, 2017. google schola A. Pektas and T. Acarman, Identification of Application in Encrypted Traffic by Using Machine Learning, Proc. of the International Conference on Man?Machine Interactions, pp. 545-554, 2017. google schola K. P. Subedi, D. R. Budhathoki, and D. Dasgupta, Forensic Analysis of Ransomware Families using Static and Dynamic Analysis, Proc. of the 2018 IEEE Symposium on Security and Privacy Workshop, pp. 180-185, 2018. google schola Y. Dodis and A. Smith, Entropic Security and the Encryption of High Entropy Messages, Proc. of the 2nd International Conference on Theory of Cryptography, pp. 556-577, 2005. google schola

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