miscellaneous-4

Android Malware Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in android-malware-detection-4

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Libraries

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Use these libraries to find android-malware-detection-4 models and implementations

Datasets

Appdroid

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Most implemented papers

Continuous Learning for Android Malware Detection

David A. Wagner, Yizheng Chen, Zhoujie Ding•Tue Feb 07 2023

This paper proposes a new hierarchical contrastive learning scheme, and a new sample selection technique to continuously train the Android malware classifier, and shows that this approach maintains more consistent performance across a seven-year time period than past methods.

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AndrODet: An adaptive Android obfuscation detector

Omid Mirzaei, J. M. D. Fuentes, Juan E. Tapiador, L. González-Manzano•Mon Dec 31 2018

AndrODet is proposed, a mechanism to detect three popular types of obfuscation in Android applications, namely identifier renaming, string encryption, and control flow obfuscation that leverages online learning techniques, thus being suitable for resource-limited environments that need to operate in a continuous manner.

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Why an Android App is Classified as Malware? Towards Malware Classification Interpretation

Cuiyun Gao, Michael R. Lyu, Bozhi Wu, Sen Chen, Lingling Fan, Yang Liu, W. Wen•Thu Apr 23 2020

A novel and interpretable ML-based approach to classify malware with high accuracy and explain the classification result meanwhile, and it is found that XMal is able to reveal the malicious behaviors more accurately.

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Deep Learning for Android Malware Defenses: A Systematic Literature Review

C. Tantithamthavorn, Yepang Liu, Yue Liu, Li Li•Mon Mar 08 2021

A systematic literature review was conducted to search and analyze how DL approaches have been applied in the context of malware defenses in the Android environment and reveals that, while the majority of these sources mainly consider DL-based Android malware detection, 53 primary studies (40.1%) design defense approaches based on other scenarios.

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Heterogeneous Temporal Graph Transformer: An Intelligent System for Evolving Android Malware Detection

Yanfang Ye, Shifu Hou, Yujie Fan, Mingxuan Ju, Wenqiang Wan, Kui Wang, Y. Mei, Qi Xiong•Fri Aug 13 2021

This paper proposes and develops an intelligent system named Dr.Droid to jointly model malware propagation and evolution for their detection at the first attempt, and proposes a novel heterogeneous temporal graph transformer framework (denoted as HTGT) to integrate both spatial and temporal dependencies while preserving the heterogeneity.

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DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode

Jacques Klein, N. Daoudi, Jordan Samhi, A. Kaboré, Kevin Allix, Tegawend'e F. Bissyand'e•Sat Sep 04 2021

This work-in-progress paper proposes DexRay, which converts the bytecode of the app DEX files into grey-scale"vector"images and feeds them to a 1-dimensional Convolutional Neural Network model, and views DexRay as foundational due to the exceedingly basic nature of the design choices.

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Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?

Shouhuai Xu, Deqiang Li, Tian Qiu, Shuo Chen, Qianmu Li•Sun Sep 19 2021

Predictive uncertainty indeed helps achieve reliable malware detection in the presence of dataset shift, but cannot cope with adversarial evasion attacks; approximate Bayesian methods are promising to calibrate and generalize malware detectors to deal with dataset shift; but can not cope with adversary evasion attacks.

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Towards a fair comparison and realistic evaluation framework of android malware detectors based on static analysis and machine learning

Borja Molina-Coronado, U. Mori, A. Mendiburu, J. Miguel-Alonso•Tue May 24 2022

An analysis of 10 influential research works on Android malware detection using a common evaluation framework concludes that the studied ML-based detectors have been evaluated optimistically, which justifies the good published results.

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Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

Ping He, Yifan Xia, Xuhong Zhang, Shouling Ji•Mon Sep 04 2023

AdvDroidZero is introduced, an efficient query-based attack framework against ML-based AMD methods that operates under the zero knowledge setting that is effective against various mainstream ML- based AMD methods, in particular, state-of-the-art such methods and real-world antivirus solutions.

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