A Co-Design Adaptive Defense Scheme With Bounded Security Damages Against Heartbleed-Like Attacks | 0 | 0.34 | 2021 |
Adaptive Cyber Defense Against Multi-Stage Attacks Using Learning-Based POMDP | 1 | 0.35 | 2020 |
Quantifying DNN Model Robustness to the Real-World Threats | 0 | 0.34 | 2020 |
DeepReturn: A deep neural network can learn how to detect previously-unseen ROP payloads without using any heuristics. | 0 | 0.34 | 2020 |
On convergence rates of game theoretic reinforcement learning algorithms. | 0 | 0.34 | 2019 |
Feedback control can make data structure layout randomization more cost-effective under zero-day attacks. | 0 | 0.34 | 2018 |
ROPNN: Detection of ROP Payloads Using Deep Neural Networks. | 1 | 0.34 | 2018 |
Online Algorithms for Adaptive Cyber Defense on Bayesian Attack Graphs. | 0 | 0.34 | 2017 |
What You See is Not What You Get! Thwarting Just-in-Time ROP with Chameleon | 0 | 0.34 | 2017 |
Towards a Science for Adaptive Defense: Revisit Server Protection | 0 | 0.34 | 2016 |
Reinforcement Learning Algorithms for Adaptive Cyber Defense against Heartbleed | 5 | 0.46 | 2014 |