9 citations · 21 across the 7 of their papers we have counts for
8 papers
Q-HyViT: Post-Training Quantization of Hybrid Vision Transformers with Bridge Block Reconstruction for IoT Systems
Jemin Lee, Yongin Kwon, Sihyeong Park +3
Recently, vision transformers (ViTs) have superseded convolutional neural networks in numerous applications, including classification, detection, and segmentation. However, the hig…
Reinforced Edge Selection using Deep Learning for Robust Surveillance in Unmanned Aerial Vehicles
Soohyun Park, Jeman Park, David Mohaisen +1
In this paper, we propose a novel deep Q-network (DQN)-based edge selection algorithm designed specifically for real-time surveillance in unmanned aerial vehicle (UAV) networks. Th…
On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study
Jaesung Yoo, Jeman Park, An Wang +2
Generative Adversarial Network (GAN) is a useful type of Neural Networks in various types of applications including generative models and feature extraction. Various types of GANs…
Domain Name System Security and Privacy: A Contemporary Survey
Aminollah Khormali, Jeman Park, Hisham Alasmary +2
The domain name system (DNS) is one of the most important components of today's Internet, and is the standard naming convention between human-readable domain names and machine-rout…
A Privacy-Preserving Longevity Study of Tor's Hidden Services
Amirali Sanatinia, Jeman Park, Erik-Oliver Blass +2
Tor and hidden services have emerged as a practical solution to protect user privacy against tracking and censorship. At the same time, little is known about the lifetime and natur…
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems
Ahmed Abusnaina, Aminollah Khormali, Hisham Alasmary +4
The main goal of this study is to investigate the robustness of graph-based Deep Learning (DL) models used for Internet of Things (IoT) malware classification against Adversarial L…
Analyzing, Comparing, and Detecting Emerging Malware: A Graph-based Approach
Hisham Alasmary, Aminollah Khormali, Afsah Anwar +4
The growth in the number of Android and Internet of Things (IoT) devices has witnessed a parallel increase in the number of malicious software (malware), calling for new analysis a…
Network-based Analysis and Classification of Malware using Behavioral Artifacts Ordering
Aziz Mohaisen, Omar Alrawi, Jeman Park +3
Using runtime execution artifacts to identify malware and its associated family is an established technique in the security domain. Many papers in the literature rely on explicit f…