ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluation

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.

Malware Analysis Malware Detection

Examining Adversarial Learning against Graph-based IoT Malware Detection Systems

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).

Adversarial Attack General Classification +2