Abstract is missing.
- Randomization-based Defenses against Data-Oriented AttacksStijn Volckaert. 1-2 [doi]
- What's in the box: Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint ModelsSahar Abdelnabi, Mario Fritz. 3-12 [doi]
- Combinatorial Boosting of Classifiers for Moving Target Defense Against Adversarial Evasion AttacksRauf Izmailov, Peter Lin, Sridhar Venkatesan, Shridatt Sugrim. 13-21 [doi]
- Game Theoretic Models for Cyber DeceptionFei Fang. 23-24 [doi]
- Using Honeypots to Catch Adversarial Attacks on Neural NetworksShawn Shan. 25 [doi]
- Research Frontiers for Moving Target DefensesNathan Burow. 27-28 [doi]
- Moving Target Defense against Adversarial Machine LearningAnshuman Chhabra, Prasant Mohapatra. 29-30 [doi]
- Themis: Ambiguity-Aware Network Intrusion Detection based on Symbolic Model ComparisonZhongjie Wang 0002, Shitong Zhu, Keyu Man, Pengxiong Zhu, Yu Hao, Zhiyun Qian, Srikanth V. Krishnamurthy, Tom La Porta, Michael J. De Lucia. 31-32 [doi]
- Concolic Execution of NMap Scripts for Honeyfarm GenerationZhe Li 0014, Bo Chen 0008, Wu-chang Feng, Fei Xie. 33-42 [doi]