Mate! Are You Really Aware? An Explainability-Guided Testing Framework
for Robustness of Malware Detectors

Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors

19 November 2021

Shuo Wang

Haojin Zhu

Papers citing "Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors"

6 / 6 papers shown

Title
FCGHunter: Towards Evaluating Robustness of Graph-Based Android Malware Detection Shiwen Song Xiaofei Xie Ruitao Feng Qi Guo Sen Chen AAML 34 0 0 28 Apr 2025
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems Ping He Lorenzo Cavallaro Shouling Ji AAML 41 0 0 23 Jan 2025
Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation Saiful Islam Salim Rubin Yuchan Yang Alexander Cooper Suryashree Ray Saumya Debray Sazzadur Rahaman AAML 42 0 0 12 Oct 2024
A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research Sicong Cao Xiaobing Sun Ratnadira Widyasari David Lo Xiaoxue Wu ... Jiale Zhang Bin Li Wei Liu Di Wu Yixin Chen 26 6 0 26 Jan 2024
RAI4IoE: Responsible AI for Enabling the Internet of Energy Minhui Xue Surya Nepal Ling Liu Subbu Sethuvenkatraman Xingliang Yuan Carsten Rudolph Ruoxi Sun Greg Eisenhauer 27 4 0 20 Sep 2023
Not Seen, Not Heard in the Digital World! Measuring Privacy Practices in Children's Apps Ruoxi Sun Minhui Xue Gareth Tyson Shuo Wang S. Çamtepe Surya Nepal 30 8 0 16 Mar 2023