Android Malware Detection
13 papers with code • 0 benchmarks • 1 datasets
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Latest papers
Crystal ball: From innovative attacks to attack effectiveness classifier
This study presents a set of innovative problem-based evasion attacks against well-known Android malware detection systems, which decrease their detection rate by up to 97%.
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
seu-proactivesecurity-group/malpurifier
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Experimental results on two Android malware datasets demonstrate that MalPurifier outperforms the state-of-the-art defenses, and it significantly strengthens the vulnerable malware detector against 37 evasion attacks, achieving accuracies over 90. 91%.
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.
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers.
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.
Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning
As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware.
MaMaDroid2.0 -- The Holes of Control Flow Graphs
The changes in the ratio between benign and malicious samples have a clear effect on each one of the models, resulting in a decrease of more than 40% in their detection rate.
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).
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.
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.
