1 |
K. Grosse, et al. "Adversarial examples for malware detection." European symposium on research in computer security. Springer, Cham, pp. 62-79. Aug. 2017.
|
2 |
W. Guo, et al. "Lemna: Explaining deep learning based security applications." Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. pp. 364-379. Oct. 2018.
|
3 |
N. McLaughlin, et al. "Deep android malware detection." Proceedings of the seventh ACM on conference on data and application security and privacy. pp. 301-308. Mar. 2017.
|
4 |
Z. Li, et al. "Vuldeepecker: A deep learning-based system for vulnerability detection." arXiv preprint arXiv:1801.01681. Jan. 2018.
|
5 |
A. Daniel, et al. "Drebin: Efficient and explainable detection of android malware in your pocket"." Proceedings of 21th Annual Network and Distributed System Security Symposium (NDSS). Feb. 2014.
|
6 |
C. Smutz, and A. Stavrou. "Malicious PDF detection using metadata and structural features." Proceedings of the 28th annual computer security applications conference. pp. 239-248. Dec. 2012.
|
7 |
Y. Zhou, and X. Jiang. "Dissecting android malware: Characterization and evolution." 2012 IEEE symposium on security and privacy. IEEE, pp. 95-109. May. 2012.
|
8 |
S. Hochreiter, and J. Schmidhuber. "Long short-term memory." Neural computation. 9(8). Nov. 1997.
|
9 |
T. Mikolov, et al. "Efficient estimation of word representations in vector space." arXiv preprint arXiv:1301.3781. Sep. 2013
|
10 |
H. Kim, et al. "Cost-Effective Valuable Data Detection Based on the Reliability of Artificial Intelligence." IEEE Access. pp. 108959-108974. Sept, 2021.
|
11 |
MT. Ribeiro, S. Singh, and C. Guestrin. ""Why should i trust you?" Explaining the predictions of any classifier." Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp. 1135-1144. Feb. 2016.
|
12 |
A. Warnecke, et al. "Evaluating explanation methods for deep learning in security." 2020 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, pp. 158-174. Sept. 2020.
|
13 |
Z. Xi, and G. Panoutsos. "Interpretable Convolutional Neural Networks Using a Rule-Based Framework for Classification." Intelligent Systems: Theory, Research and Innovation in Applications. 864. 2020.
|
14 |
C. Beek. et al, "McAfee Labs Threat Report August 2019," Mcfee Labs. rep. 2019.
|
15 |
하연 편집부, 설명가능한 인공지능(XAI) 기술 동향과 데이터 산업의 시장 전망. 하연. 2021.
|
16 |
D. Gunning and D. Aha. "DARPA's explainable artificial intelligence (XAI) program." AI Magazine. 40(2). Jun. 2019.
|
17 |
W. Samek, T. Wiegand, and KR. Muller. "Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models." arXiv preprint arXiv:1708.08296. Aug. 2017.
|
18 |
A. Das, and P. Rad. "Opportunities and challenges in explainable artificial intelligence (xai): A survey." arXiv preprint arXiv:2006.11371. Jun. 2020.
|
19 |
AB. Arrieta, et al. "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI." Information Fusion. 58. pp. 82-115. Dec. 2019.
DOI
|
20 |
J. Vaughan, et al. "Explainable neural networks based on additive index models." arXiv preprint arXiv:1806.01933. Jun. 2018.
|
21 |
A. Mills, T. Spyridopoulos and P. Legg. "Efficient and interpretable real-time malware detection using random-forest." 2019 International conference on cyber situational awareness, data analytics and assessment (Cyber SA). IEEE, pp. 1-8. Nov. 2019.
|
22 |
S. Bach, et al. "On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation." PloS one. 10(7). July. 2015.
|
23 |
A. Kuppan, and NA. Le-Khac. "Black box attacks on explainable artificial intelligence (XAI) methods in cyber security." 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8. Sep. 2020.
|
24 |
B. Zhou, et al. "Learning deep features for discriminative localization." Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2921-2929. Dec. 2016.
|
25 |
G. Iadarola, et al. "Evaluating deep learning classification reliability in android malware family detection." 2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE, pp. 255-260. Oct. 2020.
|
26 |
M. Kinkead, et al. "Towards Explainable CNNs for Android Malware Detection." Procedia Computer Science. 184. pp. 959-965. Mar. 2021.
DOI
|
27 |
LS. Shapley. "17. A value for n-person games." Princeton University Press, 2016.
|
28 |
SM, Lundberg and S. Lee. "A unified approach to interpreting model predictions." Proceedings of the 31st international conference on neural information processing systems. pp.4768-4777. May. 2017.
|
29 |
M. Wang, et al, "An explainable machine learning framework for intrusion detection systems," IEEE Access, 8, pp. 73127-73141, Apr. 2020,
DOI
|