no code implementations • 5 Oct 2023 • Ahmed Abusnaina, Yizhen Wang, Sunpreet Arora, Ke Wang, Mihai Christodorescu, David Mohaisen
Highlighting volatile information channels within the software, we introduce three software pre-processing steps to eliminate the attack surface, namely, padding removal, software stripping, and inter-section information resetting.
no code implementations • 30 Aug 2021 • Ahmed Abusnaina, Afsah Anwar, Sultan Alshamrani, Abdulrahman Alabduljabbar, RhongHo Jang, DaeHun Nyang, David Mohaisen
Additionally, our analysis of the industry-standard malware detectors shows their instability to the malware mutations.
no code implementations • 3 Mar 2021 • Sultan Alshamrani, Ahmed Abusnaina, Mohammed Abuhamad, DaeHun Nyang, David Mohaisen
Social media has become an essential part of the daily routines of children and adolescents.
no code implementations • ICCV 2021 • Ahmed Abusnaina, Yuhang Wu, Sunpreet Arora, Yizhen Wang, Fei Wang, Hao Yang, David Mohaisen
We present the first graph-based adversarial detection method that constructs a Latent Neighborhood Graph (LNG) around an input example to determine if the input example is adversarial.
no code implementations • 23 Jan 2020 • Mohammed Abuhamad, Ahmed Abusnaina, DaeHun Nyang, David Mohaisen
This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits.
no code implementations • 20 Sep 2019 • Aminollah Khormali, Ahmed Abusnaina, Songqing Chen, DaeHun Nyang, Aziz Mohaisen
Therefore, we proposed an approach to generate adversarial examples, COPYCAT, which is specifically designed for malware detection systems considering two main goals; achieving a high misclassification rate and maintaining the executability and functionality of the original input.
no code implementations • 12 Feb 2019 • Ahmed Abusnaina, Aminollah Khormali, Hisham Alasmary, Jeman Park, Afsah Anwar, Ulku Meteriz, Aziz Mohaisen
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 Learning (AL).