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Deep learning classifiers achieve state-of-the-art performance in various risk detection applications.
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DeepAID: Interpreting and Improving Deep Learning-Based Anomaly Detection in Security Applications. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security
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Binlin Cheng, Jiang Ming, Jianmin Fu, Guojun Peng, Ting Chen, Xiaosong Zhang, and Jean-Yves Marion. 2018 · 2018
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Lemna: Explaining deep learning based security applications. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
Wenbo Guo, Dongliang Mu, Jun Xu, Purui Su, Gang Wang, and Xinyu Xing. 2018 · 2018
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A multimodal deep learning method for android malware detection using various features
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Vuldeepecker: A deep learning-based system for vulnerability detection
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Rethinking Explainable Machines: The GDPR’s’ Right to Explanation’Debate and the Rise of Algorithmic Audits in Enterprise
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Techniques for interpretable machine learning
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XAI—Explainable artificial intelligence
David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, and Guang-Zhong Yang. 2019 · 2019
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DeepBalance: Deep-learning and fuzzy oversampling for vulnerability detection
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Vipin Pillai and Hamed Pirsiavash. 2021 · 2021
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{ \{ Explanation-Guided } \} Backdoor Poisoning Attacks Against Malware Classifiers. In 30th USENIX Security Symposium (USENIX Security 21)
Giorgio Severi, Jim Meyer, Scott Coull, and Alina Oprea. 2021 · 2021
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CADE: Detecting and Explaining Concept Drift Samples for Security Applications. In 30th USENIX Security Symposium (USENIX Security 21)
Limin Yang, Wenbo Guo, Qingying Hao, Arridhana Ciptadi, Ali Ahmadzadeh, Xinyu Xing, and Gang Wang. 2021 · 2021
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ExCon: Explanation-driven supervised contrastive learning for image classification
Zhibo Zhang, Jongseong Jang, Chiheb Trabelsi, Ruiwen Li, Scott Sanner, Yeonjeong Jeong, and Dongsub Shim. 2021 · 2021
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Dos and don’ts of machine learning in computer security. In Proc. of the USENIX Security Symposium
Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck. 2022 · 2022
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Mohamed Karim Belaid, Eyke Hüllermeier, Maximilian Rabus, and Ralf Krestel. 2022 · 2022
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Explanation-guided fairness testing through genetic algorithm. In Proceedings of the 44th International Conference on Software Engineering
Ming Fan, Wenying Wei, Wuxia Jin, Zijiang Yang, and Ting Liu. 2022 · 2022
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MsDroid: Identifying Malicious Snippets for Android Malware Detection
Yiling He, Yiping Liu, Lei Wu, Ziqi Yang, Kui Ren, and Zhan Qin. 2022 · 2022
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XAI Systems Evaluation: A Review of Human and Computer-Centred Methods
Pedro Lopes, Eduardo Silva, Cristiana Braga, Tiago Oliveira, and Luís Rosado. 2022 · 2022
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A rigorous study of integrated gradients method and extensions to internal neuron attributions. In International Conference on Machine Learning
Daniel D Lundstrom, Tianjian Huang, and Meisam Razaviyayn. 2022 · 2022
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Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience
Antonios Mamalakis, Elizabeth A Barnes, and Imme Ebert-Uphoff. 2022 · 2022
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RE-Mind: a First Look Inside the Mind of a Reverse Engineer. In 31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10-12, 2022
Alessandro Mantovani, Simone Aonzo, Yanick Fratantonio, and Davide Balzarotti. 2022 · 2022
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Jadeite: A novel image-behavior-based approach for java malware detection using deep learning
Islam Obaidat, Meera Sridhar, Khue M Pham, and Phu H Phung. 2022 · 2022
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Explainable Artificial Intelligence for Cyber Threat Intelligence (XAI-CTI)
Sagar Samtani, Hsinchun Chen, Murat Kantarcioglu, and Bhavani Thuraisingham. 2022 · 2022
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Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations. In International Conference on Artificial Intelligence and Statistics
Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu, and Pradeep Ravikumar. 2022 · 2022
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