Search Results for author: Ahmed Abusnaina

Found 7 papers, 0 papers with code

Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations

no code implementations5 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.

Malware Detection

Adversarial Example Detection Using Latent Neighborhood Graph

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.

Adversarial Attack Graph Attention

Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Contemporary Survey

no code implementations23 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.

COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection

no code implementations20 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.

Adversarial Attack Malware Detection

Examining Adversarial Learning against Graph-based IoT Malware Detection Systems

no code implementations12 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).

Adversarial Attack General Classification +2

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