Android Malware Detection
8 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Android Malware Detection
Most implemented papers
Continuous Learning for Android Malware Detection
wagner-group/active-learning
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We propose a new hierarchical contrastive learning scheme, and a new sample selection technique to continuously train the Android malware classifier.
AndrODet: An Adaptive Android Obfuscation Detector
This is typically applied to protect intellectual property in benign apps, or to hinder the process of extracting actionable information in the case malware.
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
In this paper, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal) to classify malware with high accuracy and explain the classification result meanwhile.
Deep Learning for Android Malware Defenses: a Systematic Literature Review
yueyueL/DL-based-Android-Malware-Defenses-review
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In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment.
heterogeneous temporal graph transformer: an intelligent system for evolving android malware detection
kdd2021drdroid/KDD2021_DrDroid
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KDD 2021
To capture malware evolution, we further consider the temporal dependence and introduce a heterogeneous temporal graph to jointly model malware propagation and evolution by considering heterogeneous spatial dependencies with temporal dimensions.
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
trustworthy-software/dexray
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This work-in-progress paper contributes to the domain of Deep Learning based Malware detection by providing a sound, simple, yet effective approach (with available artefacts) that can be the basis to scope the many profound questions that will need to be investigated to fully develop this domain.
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?
deqangss/malware-uncertainty
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Our main findings are: (i) predictive uncertainty indeed helps achieve reliable malware detection in the presence of dataset shift, but cannot cope with adversarial evasion attacks; (ii) approximate Bayesian methods are promising to calibrate and generalize malware detectors to deal with dataset shift, but cannot cope with adversarial evasion attacks; (iii) adversarial evasion attacks can render calibration methods useless, and it is an open problem to quantify the uncertainty associated with the predicted labels of adversarial examples (i. e., it is not effective to use predictive uncertainty to detect adversarial examples).
Efficient Concept Drift Handling for Batch Android Malware Detection Models
Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: 1) the frequency with which the models are retrained, and 2) the data used for retraining.
