Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?

Author:

Liu Yue1,Tantithamthavorn Chakkrit1,Li Li1,Liu Yepang2

Affiliation:

1. Monash University,Faculty of Information Technology,Melbourne,Australia

2. Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China

Publisher

IEEE

Reference54 articles.

1. Transcend: Detecting concept drift in malware clas-sification models;jordaney;26th USENIX Security Symposium,2017

2. Dataset bias in android malware detection;lin;ArXiv Preprint,2022

3. Anchors: High-Precision Model-Agnostic Explanations

4. "Why Should I Trust You?"

5. Neural machine translation by jointly learning to align and translate;bahdanau;ArXiv Preprint,2014

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