The paper proposes a time-aware self-supervised learning framework using BYOL to improve Android malware detection robustness by accurately accounting for app release times.

The paper proposes a privacy-by-design pipeline for Android malware detection that achieves strong performance by avoiding the collection of sensitive user data entirely.

The paper introduces Praxis, a behavior-oriented Android malware detection system that hypothesizes, confirms, and judges malicious behaviors.

The paper introduces McNdroid, a large longitudinal multimodal benchmark for Android malware, demonstrating that temporal drift significantly degrades detection performance, which is best mitigated by…

This paper tests different INT8 quantized Multi-Layer Perceptron models for malware detection on Android devices, achieving high accuracy with reduced energy consumption.

MARD introduces a multi-agent framework that combines Large Language Models (LLMs) with traditional static analysis engines to achieve robust and highly interpretable Android malware detection with lo…

This paper introduces a novel malware detection system for macOS by utilizing domain-specific static features, achieving state-of-the-art performance and demonstrating strong generalization capabiliti…

The paper proposes a model-agnostic framework to evaluate combining Active Learning (AL) and Semi-Supervised Learning (SSL) techniques for malware detection, demonstrating that these combined methods…

The paper proposes SEED, a novel semantic-structure-agnostic semi-supervised continual learning method that significantly improves malware detection performance under limited labeling by leveraging re…

The paper proposes a cost-aware, adaptive maintenance framework using Reinforcement Learning (RL) and self-supervised learning to mitigate performance degradation (concept drift) in Android malware de…

FreeMOCA is a memory- and compute-efficient continual learning framework that uses adaptive layer-wise interpolation in parameter space to prevent catastrophic forgetting when analyzing evolving malwa…

This study longitudinally evaluates the adversarial robustness of Android malware detection systems over a decade, finding that temporal separation significantly degrades robustness due to concept dri…

The paper introduces a static analysis pipeline using graph kernels to automatically attribute unknown Android proxy malware to specific commercial proxy networks with high accuracy.

AndroScanner is an automated pipeline that detects backend vulnerabilities in Android applications by combining static and dynamic analysis, successfully identifying a zero-day Excessive Data Exposure…

This paper proposes a hybrid feature fusion and voting-based approach for automated malware detection and classification into specific malware families, achieving high performance metrics like an AUC…

This paper investigates the capability of Large Language Models (LLMs) to detect obfuscation in Android apps through semantic reasoning.

WOOTdroid is a novel, non-invasive system for comprehensive on-device tracing on stock Android that simultaneously addresses syscall data loss and the semantic gap in Binder IPC events.

The paper investigates improving 43-class malware type classification on MalNet-Image Tiny by evaluating the combined effects of multi-scale feature fusion, transfer learning, advanced data augmentati…

AsmRAG is a novel framework that improves malware detection by treating it as an evidence-based retrieval task using a code-specialized LLM, achieving high accuracy while providing transparent forensi…

The paper introduces Trident, a novel malware detection system that combines static features, LLM-derived behavioral rules, and direct LLM analysis to achieve superior robustness against concept drift…