7 citations · 26 across the 5 of their papers we have counts for
6 papers
Generating Practical Adversarial Network Traffic Flows Using NIDSGAN
Bolor-Erdene Zolbayar, Ryan Sheatsley, Patrick McDaniel +4
Network intrusion detection systems (NIDS) are an essential defense for computer networks and the hosts within them. Machine learning (ML) nowadays predominantly serves as the basi…
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations
Shasha Li, Abhishek Aich, Shitong Zhu +4
When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied. This could be possible because,…
You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning
Shitong Zhu, Shasha Li, Zhongjie Wang +5
As Deep Packet Inspection (DPI) middleboxes become increasingly popular, a spectrum of adversarial attacks have emerged with the goal of evading such middleboxes. Many of these att…
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
Shasha Li, Shitong Zhu, Sudipta Paul +5
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target ob…
A4 : Evading Learning-based Adblockers
Shitong Zhu, Zhongjie Wang, Xun Chen +6
Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful. As a result, there have recently emer…
AdGraph: A Graph-Based Approach to Ad and Tracker Blocking
Umar Iqbal, Peter Snyder, Shitong Zhu +3
User demand for blocking advertising and tracking online is large and growing. Existing tools, both deployed and described in research, have proven useful, but lack either the comp…