1.3k citations · 2.7k across the 38 of their papers we have counts for
9 papers · 1 filter
Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap
Somali Chaterji, Parinaz Naghizadeh, Muhammad Ashraful Alam +14
Cyberphysical systems (CPS) are ubiquitous in our personal and professional lives, and they promise to dramatically improve micro-communities (e.g., urban farms, hospitals), macro-…
Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures
Saurabh Bagchi, Vaneet Aggarwal, Somali Chaterji +13
A set of about 80 researchers, practitioners, and federal agency program managers participated in the NSF-sponsored Grand Challenges in Resilience Workshop held on Purdue campus on…
Cost-Aware Robust Tree Ensembles for Security Applications
Yizheng Chen, Shiqi Wang, Weifan Jiang +2
There are various costs for attackers to manipulate the features of security classifiers. The costs are asymmetric across features and to the directions of changes, which cannot be…
CLN2INV: Learning Loop Invariants with Continuous Logic Networks
Gabriel Ryan, Justin Wong, Jianan Yao +2
Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the…
Fine Grained Dataflow Tracking with Proximal Gradients
Gabriel Ryan, Abhishek Shah, Dongdong She +2
Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel…
Neutaint: Efficient Dynamic Taint Analysis with Neural Networks
Dongdong She, Yizheng Chen, Abhishek Shah +2
Dynamic taint analysis (DTA) is widely used by various applications to track information flow during runtime execution. Existing DTA techniques use rule-based taint-propagation, wh…
Towards Compact and Robust Deep Neural Networks
Vikash Sehwag, Shiqi Wang, Prateek Mittal +1
Deep neural networks have achieved impressive performance in many applications but their large number of parameters lead to significant computational and storage overheads. Several…
Enhancing Gradient-based Attacks with Symbolic Intervals
Shiqi Wang, Yizheng Chen, Ahmed Abdou +1
Recent breakthroughs in defenses against adversarial examples, like adversarial training, make the neural networks robust against various classes of attackers (e.g., first-order gr…
On Training Robust PDF Malware Classifiers
Yizheng Chen, Shiqi Wang, Dongdong She +1
Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that…