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…

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…

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.

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

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 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…

The paper constructs a large, adversarial malware dataset from real-world binaries, demonstrating high evasion rates and showing that even small amounts of poisoned data can severely compromise malwar…

The paper proposes a zero-label malware family classification framework that uses a weighted hierarchical ensemble of large language models (LLMs) to classify malware without requiring labeled trainin…

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.

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…

The paper introduces the first byte-native Large Language Model (LLM) capable of analyzing raw executable binary data, achieving high accuracy in tasks like malware and architecture classification.

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

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.

This paper proposes an explainability-guided adversarial attack that successfully evades transformer-based malware detectors by perturbing the most influential components of the control flow graph rep…

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

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…

The paper proposes a novel method to generate adversarial malware samples that evade deep learning detectors while simultaneously minimizing the detectable 'drift' signals, showing that similarity con…

MalwarePT introduces a novel binary-level foundation model, pretrained on Windows PE code-section bytes using a ModernBERT-style encoder, demonstrating superior transfer learning capabilities across v…

The paper proposes VulGNN, a lightweight Graph Neural Network (GNN) model, which achieves vulnerability detection performance comparable to large language models (LLMs) while being significantly small…

The paper proposes an explainable attention-based LSTM framework to achieve early and reliable detection of advanced, AI-assisted ransomware by analyzing file system behavioral sequences.