109 citations · 247 across the 11 of their papers we have counts for
11 papers
DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection
Yizheng Chen, Zhoujie Ding, Lamya Alowain +2
We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes fro…
Continuous Learning for Android Malware Detection
Yizheng Chen, Zhoujie Ding, David Wagner
Machine learning methods can detect Android malware with very high accuracy. However, these classifiers have an Achilles heel, concept drift: they rapidly become out of date and in…
Learning Security Classifiers with Verified Global Robustness Properties
Yizheng Chen, Shiqi Wang, Yue Qin +3
Many recent works have proposed methods to train classifiers with local robustness properties, which can provably eliminate classes of evasion attacks for most inputs, but not all…
A Survey on Contact Tracing: the Latest Advancements and Challenges
Ting Jiang, Yang Zhang, Minhao Zhang +7
Infectious diseases are caused by pathogenic microorganisms, such as bacteria, viruses, parasites or fungi, which can be spread, directly or indirectly, from one person to another.…
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…
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…
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…
MixTrain: Scalable Training of Verifiably Robust Neural Networks
Shiqi Wang, Yizheng Chen, Ahmed Abdou +1
Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks. The most promising defenses, adversarially robust training a…
Practical Attacks Against Graph-based Clustering
Yizheng Chen, Yacin Nadji, Athanasios Kountouras +4
Graph modeling allows numerous security problems to be tackled in a general way, however, little work has been done to understand their ability to withstand adversarial attacks. We…
Hiding in Plain Sight: A Longitudinal Study of Combosquatting Abuse
Panagiotis Kintis, Najmeh Miramirkhani, Charles Lever +5
Domain squatting is a common adversarial practice where attackers register domain names that are purposefully similar to popular domains. In this work, we study a specific type of…