Android Malware Detection
13 papers with code • 0 benchmarks • 1 datasets
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Latest papers with no code
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
This research explores the effectiveness of utilizing GAN-generated data to train a model for the detection of Android malware.
Unraveling the Key of Machine Learning Solutions for Android Malware Detection
Android malware detection serves as the front line against malicious apps.
ActDroid: An active learning framework for Android malware detection
The growing popularity of Android requires malware detection systems that can keep up with the pace of new software being released.
Android Malware Detection with Unbiased Confidence Guarantees
We examine its performance on a large-scale dataset collected by installing 1866 malicious and 4816 benign applications on a real android device.
Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection
Therefore, it needs to be determined to what extent the use of a specific obfuscation strategy or tool poses a risk for the validity of ML malware detectors for Android based on static analysis features.
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
This efficiency, coupled with its state-of-the-art performance, highlights LaFiCMIL's potential as a groundbreaking approach in the field of large file classification.
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
In this paper, we address this problem with a review of 42 highly-cited papers, spanning a decade of research (from 2011 to 2021).
Investigating Feature and Model Importance in Android Malware Detection: An Implemented Survey and Experimental Comparison of ML-Based Methods
The popularity of Android means it is a common target for malware.
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization
Such in-depth analysis motivates employing deep neural networks (DNNs) for a set of features and patterns extracted from applications to facilitate detecting potentially dangerous applications independently.
Flexible Android Malware Detection Model based on Generative Adversarial Networks with Code Tensor
Finally, a flexible Android malware detection model based on GANs with code tensor (MTFD-GANs) is proposed.
