8 citations · 17 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
Understanding Internet of Things Malware by Analyzing Endpoints in their Static Artifacts
Afsah Anwar, Jinchun Choi, Abdulrahman Alabduljabbar +7
The lack of security measures among the Internet of Things (IoT) devices and their persistent online connection gives adversaries a prime opportunity to target them or even abuse t…
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…
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…