Towards Robust Android Malware Detection Models Using Adversarial Learning

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Robust Android Malware Detection Against Adversarial Example Attacks Guide

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Details Robust Malware Detection Models: Learning From Adversarial Attacks and Defenses Guide

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Explainable AI-Based Android Malware Detection and Risk Analysis Using Machine Learning

USENIX Security '23 - Black-box Adversarial Example Attack towards FCG Based Android Malware...

1704.08996 - Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

Image-based Android Malware Detection Models using Static and Dynamic Features - ISDA 2021

Robust Malware detection from IoT Device Dataset using Deep Learning

Improving Malware detection using adversarial attacks in android systems

A Dynamic Robust DL Based Model for Android Malware Detection

NDSS 2017: MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models

ICAASE 2020 | Android Malware Detection using Convolutional Deep Neural Networks

Attacking Malware with Adversarial Machine Learning, w/ Edward Raff - #529

Robust Malware Challenge

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Last Updated: October 18, 2026

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Information Robustness of on-Device Models: Adversarial Attack to Deep Learning Models on Android Apps Update

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Summary

Towards Robust Android Malware Detection Models using Adversarial Learning Authors: Heng Li, Shiyao Zhou, Wei Yuan, Xiapu Luo, Cuiying Gao, Shuiyan Chen. The last decade witnessed an exponential growth of smartphones and their users, which has drawn massive attention from ... USENIX Security '23 - Black-box Authors: Hemant Rathore , B Raja Narasimhan , Sanjay K. Sahay and Mohit Sewak Abstract: Today smartphones are an ... Video taken during the Network and Distributed System Security (NDSS) Symposium 2017, held February 26 The 4th Edition of the International Conference on Advanced Aspects of Software Engineering (ICAASE'20) Fatima Bourabaa and ... Today we're joined by Edward Raff, chief scientist and head of the machine

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