Android Malware Detection
13 papers with code • 0 benchmarks • 1 datasets
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Level Up with RealAEs: Leveraging Domain Constraints in Feature Space to Strengthen Robustness of Android Malware Detection
Realistic attacks in the Android malware domain create Realizable Adversarial Examples (RealAEs), i. e., AEs that satisfy the domain constraints of Android malware.
A two-steps approach to improve the performance of Android malware detectors
For the subset of "difficult" samples, we rely on GUIDED RETRAINING, which leverages the correct predictions and the errors made by the base malware detector to guide the retraining process.
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection
We propose the Malceiver, a hierarchical Perceiver model for Android malware detection that makes use of multi-modal features.
Android Malware Detection using Feature Ranking of Permissions
We investigate the use of Android permissions as the vehicle to allow for quick and effective differentiation between benign and malware apps.
Graph Neural Network-based Android Malware Classification with Jumping Knowledge
This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK).
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector
In the following experiments, we compare the explainability and fidelity of our proposed method with state-of-the-arts, respectively.
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
The proposed manipulation technique is a query-efficient optimization algorithm that can find and inject optimal sequences of transformations into malware apps.
Data Augmentation for Opcode Sequence Based Malware Detection
In this paper we study data augmentation for opcode sequence based Android malware detection.
Detection of Malicious Android Applications: Classical Machine Learning vs. Deep Neural Network Integrated with Clustering
Today anti-malware community is facing challenges due to the ever-increasing sophistication and volume of malware attacks developed by adversaries.
Identification of Significant Permissions for Efficient Android Malware Detection
In this paper, we performed a comprehensive feature analysis to identify the significant Android permissions and propose an efficient Android malware detection system using machine learning and deep neural network.
