21 citations · 40 across the 11 of their papers we have counts for
11 papers
Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations
Ahmed Abusnaina, Yizhen Wang, Sunpreet Arora +3
Toward robust malware detection, we explore the attack surface of existing malware detection systems. We conduct root-cause analyses of the practical binary-level black-box adversa…
Measuring and Modeling the Free Content Web
Abdulrahman Alabduljabbar, Runyu Ma, Ahmed Abusnaina +4
Free content websites that provide free books, music, games, movies, etc., have existed on the Internet for many years. While it is a common belief that such websites might be diff…
ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluation
Ahmed Abusnaina, Afsah Anwar, Sultan Alshamrani +4
The rapid growth of the Internet of Things (IoT) devices is paralleled by them being on the front-line of malicious attacks. This has led to an explosion in the number of IoT malwa…
ShellCore: Automating Malicious IoT Software Detection by Using Shell Commands Representation
Hisham Alasmary, Afsah Anwar, Ahmed Abusnaina +6
The Linux shell is a command-line interpreter that provides users with a command interface to the operating system, allowing them to perform a variety of functions. Although very u…
Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube
Sultan Alshamrani, Ahmed Abusnaina, Mohammed Abuhamad +2
Social media has become an essential part of the daily routines of children and adolescents. Moreover, enormous efforts have been made to ensure the psychological and emotional wel…
Hiding in Plain Sight: A Measurement and Analysis of Kids' Exposure to Malicious URLs on YouTube
Sultan Alshamrani, Ahmed Abusnaina, David Mohaisen
The Internet has become an essential part of children's and adolescents' daily life. Social media platforms are used as educational and entertainment resources on daily bases by yo…
Cleaning the NVD: Comprehensive Quality Assessment, Improvements, and Analyses
Afsah Anwar, Ahmed Abusnaina, Songqing Chen +2
Vulnerability databases are vital sources of information on emergent software security concerns. Security professionals, from system administrators to developers to researchers, he…
A Deep Learning-based Fine-grained Hierarchical Learning Approach for Robust Malware Classification
Ahmed Abusnaina, Mohammed Abuhamad, Hisham Alasmary +5
The wide acceptance of Internet of Things (IoT) for both household and industrial applications is accompanied by several security concerns. A major security concern is their probab…
Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Contemporary Survey
Mohammed Abuhamad, Ahmed Abusnaina, DaeHun Nyang +1
Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and intera…
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…