Machine Learning Techniques for Android Malware Detection
Summary
Machine learning techniques for Android malware detection have evolved markedly over the past decade, offering sophisticated mechanisms to identify malicious applications with high accuracy and low latency. Traditional pipelines rely on static analysis, extracting code features such as permissions and API call sequences, and on dynamic analysis, monitoring behaviour during execution on emulators or real devices. Handcrafted features feed classifiers including support vector machines, decision trees and ensemble methods, while recent work leverages deep learning architectures—convolutional neural networks, recurrent neural networks and transformer models—to learn representations directly from raw or transformed data. Dynamic input generation strategies have improved coverage of hidden routines, and image-based visualisation approaches convert binaries into two-dimensional patterns for deep classifiers. Challenges remain in detecting obfuscated or polymorphic malware, handling class imbalance and preserving privacy during onsite or cloud-based analysis. Integrating static and dynamic characteristics, reweighted loss functions and few-shot learning paradigms has enhanced detection of zero-day threats, paving the way for more robust and scalable defences on mobile platforms worldwide.
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Machine Learning Techniques for Android Malware Detection publication trend
The graph below shows the total number of articles in machine learning techniques for android malware detection across all publications each year (not limited to Nature Index journals).
Technical terms
Static analysis: Examination of application code and resources without executing the programme to extract structural features.
Dynamic analysis: Monitoring of an application’s runtime behaviour within an emulated or real environment to capture operational characteristics.
Feature extraction: Process of transforming raw code or behavioural outputs into numerical vectors suitable for machine learning models.
Stateful input generation: Technique that generates inputs based on prior execution paths to trigger deeper or hidden code routines during dynamic analysis.
Reweighted loss function: Training strategy that assigns differential weights to classes in the loss computation to mitigate the impact of imbalanced datasets.
References
- Intelligent analysis of android application privacy policy and permission consistency. Artificial Intelligence Review (2024).
- A Review of Android Malware Detection Approaches Based on Machine Learning. IEEE Access (2020).
- DL-Droid: Deep learning based android malware detection using real devices. Computers & Security (2020).
- An Efficient DenseNet-Based Deep Learning Model for Malware Detection. Entropy (2021).
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