45 citations · 203 across the 20 of their papers we have counts for
6 papers · 1 filter
Quantifying Cybersecurity Effectiveness of Dynamic Network Diversity
Huashan Chen, Hasan Cam, Shouhuai Xu
The deployment of monoculture software stacks can have devastating consequences because a single attack can compromise all of the vulnerable computers in cyberspace. This one-vulne…
Quantifying Cybersecurity Effectiveness of Software Diversity
Huashan Chen, Richard B. Garcia-Lebron, Zheyuan Sun +2
The deployment of monoculture software stacks can cause a devastating damage even by a single exploit against a single vulnerability. Inspired by the resilience benefit of biologic…
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?
Deqiang Li, Tian Qiu, Shuo Chen +2
The deep learning approach to detecting malicious software (malware) is promising but has yet to tackle the problem of dataset shift, namely that the joint distribution of examples…
Towards Making Deep Learning-based Vulnerability Detectors Robust
Zhen Li, Jing Tang, Deqing Zou +5
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention. In particular, deep learning-based vulnerability detector…
Data-Driven Characterization and Detection of COVID-19 Themed Malicious Websites
Mir Mehedi Ahsan Pritom, Kristin M. Schweitzer, Raymond M. Bateman +2
COVID-19 has hit hard on the global community, and organizations are working diligently to cope with the new norm of "work from home". However, the volume of remote work is unprece…
Characterizing the Landscape of COVID-19 Themed Cyberattacks and Defenses
Mir Mehedi Ahsan Pritom, Kristin M. Schweitzer, Raymond M. Bateman +2
COVID-19 (Coronavirus) hit the global society and economy with a big surprise. In particular, work-from-home has become a new norm for employees. Despite the fact that COVID-19 can…