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ORCID12th AISec@CCS 2019: London, UK
- Lorenzo Cavallaro, Johannes Kinder, Sadia Afroz, Battista Biggio, Nicholas Carlini, Yuval Elovici, Asaf Shabtai:
Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, AISec@CCS 2019, London, UK, November 15, 2019. ACM 2019, ISBN 978-1-4503-6833-9
Privacy-preserving Machine Learning
- Stacey Truex, Nathalie Baracaldo, Ali Anwar
, Thomas Steinke, Heiko Ludwig, Rui Zhang, Yi Zhou:
A Hybrid Approach to Privacy-Preserving Federated Learning. 1-11 - Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig:
HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning. 13-23
Malware Detection
- Alireza Mohammadinodooshan, Ulf Kargén, Nahid Shahmehri:
Robust Detection of Obfuscated Strings in Android Apps. 25-35 - Rajvardhan Oak, Min Du, David Yan, Harshvardhan C. Takawale, Idan Amit:
Malware Detection on Highly Imbalanced Data through Sequence Modeling. 37-48
Machine Learning for Security
- Kenneth Alperin, Allan B. Wollaber, Dennis M. Ross, Pierre C. Trepagnier, Leslie Leonard:
Risk Prioritization by Leveraging Latent Vulnerability Features in a Contested Environment. 49-57 - Toshiki Shibahara, Hirokazu Kodera, Daiki Chiba, Mitsuaki Akiyama, Kunio Hato, Ola Söderström, Daniel Dalek, Masayuki Murata:
Cross-Vendor Knowledge Transfer for Managed Security Services with Triplet Network. 59-69 - Mohammad Ruhul Amin, Pranav Garg, Baris Coskun:
CADENCE: Conditional Anomaly Detection for Events Using Noise-Contrastive Estimation. 71-82
Adversarial Machine Learning
- Mika Juuti, Buse Gul Atli, N. Asokan:
Making Targeted Black-box Evasion Attacks Effective and Efficient. 83-94 - Alex Lamb, Vikas Verma, Juho Kannala, Yoshua Bengio:
Interpolated Adversarial Training: Achieving Robust Neural Networks Without Sacrificing Too Much Accuracy. 95-103 - Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal:
Analyzing the Robustness of Open-World Machine Learning. 105-116
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