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…

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 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 proposes a structural method using decision tree rulesets and multiple complementary metrics to detect concept drift in evolving malware families, finding that fixed-interval windowing with…

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…

The paper proposes a universal robustification framework to enhance drift-adaptive malware detectors against combined concept drift and adversarial attacks, significantly reducing attack success rates…

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…

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 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 framework to intentionally evade malware detectors by adding a small number of benign API imports, successfully demonstrating targeted misclassification into a chosen benign categ…

The paper demonstrates that static malware classifiers often rely on superficial artifacts like packing and metadata rather than true malicious semantics, using the TRUSTEE interpretability tool to di…

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…

eDySec introduces a deep learning framework for dynamic behavioral analysis that significantly improves the detection of malicious software packages in the PyPI ecosystem by enhancing stability and ex…

The paper proposes a certifiably robust malware detection framework using randomized smoothing and feature ablation to guarantee detection accuracy against metamorphic evasion attacks.

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…

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 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.

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 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…