23 citations · 57 across the 7 of their papers we have counts for
8 papers
Self-Supervised Graph Transformer for Deepfake Detection
Aminollah Khormali, Jiann-Shiun Yuan
Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. Howev…
Deep Biological Pathway Informed Pathology-Genomic Multimodal Survival Prediction
Lin Qiu, Aminollah Khormali, Kai Liu
The integration of multi-modal data, such as pathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, a…
Generating Adversarial Examples with an Optimized Quality
Aminollah Khormali, DaeHun Nyang, David Mohaisen
Deep learning models are widely used in a range of application areas, such as computer vision, computer security, etc. However, deep learning models are vulnerable to Adversarial E…
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…
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection
Aminollah Khormali, Ahmed Abusnaina, Songqing Chen +2
Despite many attempts, the state-of-the-art of adversarial machine learning on malware detection systems generally yield unexecutable samples. In this work, we set out to examine t…
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…
End-to-End Analysis of In-Browser Cryptojacking
Muhammad Saad, Aminollah Khormali, Aziz Mohaisen
In-browser cryptojacking involves hijacking the CPU power of a website's visitor to perform CPU-intensive cryptocurrency mining, and has been on the rise, with 8500% growth during…