Evaluating Rule-Based Global XAI Malware Detection Methods-Reference-Cited by-同舟云学术

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Evaluating Rule-Based Global XAI Malware Detection Methods

Published:2023 Issue: Volume: Page:3-22
ISSN:0302-9743
Container-title:Network and System Security
language:
Short-container-title:

Author:

Li Rui^ORCID,Gadyatskaya Olga^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-39828-5_1

Reference48 articles.

1. Alani, M., Awad, A.: PAIRED: an explainable lightweight Android malware detection system. IEEE Access 10, 73214–73228 (2022)

2. Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., Rieck, K.: DREBIN: effective and explainable detection of android malware in your pocket. In: Symposium on Network and Distributed System Security (NDSS) (2014)

3. Backes, M., Nauman, M.: LUNA: quantifying and leveraging uncertainty in Android malware analysis through Bayesian machine learning. In: 2017 IEEE European Symposium on Security and Privacy, Los Alamitos, CA, USA, pp. 204–217. IEEE (2017)

4. Bénard, C., Biau, G., Da Veiga, S., Scornet, E.: SIRUS: stable and interpretable rule set for classification. Electron. J. Stat. 15(1), 427–505 (2021)

5. Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 70, 245–317 (2021)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Position: The Explainability Paradox - Challenges for XAI in Malware Detection and Analysis;2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW);2024-07-08

2. SmRM: Ensemble Learning Devised Solution for Smart Riskware Management in Android Machines;2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS);2023-11-16

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