1110 matches found
DMLDroid: Deep Multimodal Fusion Framework for Android Malware Detection with Resilience to Code Obfuscation and Adversarial Perturbations
In recent years, learning-based Android malware detection has seen significant advancements, with detectors generally falling into three categories: string-based, image-based, and graph-based approaches. While these methods have shown strong detection performance, they often struggle to sustain...
Malicious code in palynology-galaxy-fusion-playwright (npm)
The package palynology-galaxy-fusion-playwright was found to contain malicious code...
Quantum AI Algorithm Development for Enhanced Cybersecurity: a Hybrid Approach to Malware Detection
This study explores the application of quantum machine learning QML algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealin...
Revisiting Third-Party Library Detection: a Ground Truth Dataset and Its Implications across Security Tasks
Accurate detection of third-party libraries TPLs is fundamental to Android security, supporting vulnerability tracking, malware detection, and supply chain auditing. Despite many proposed tools, their real-world effectiveness remains unclear.We present the first large-scale empirical study of ten...
BIDO: a Unified Approach to Address Obfuscation and Concept Drift Challenges in Image-Based Malware Detection
To identify malicious Android applications, various malware detection techniques have been proposed. Among them, image-based approaches are considered potential alternatives due to their efficiency and scalability. Recent studies have reported that these approaches suffer significant performance...
FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture
Applying deep learning to malware detection has drawn great attention due to its notable performance. With the increasing prevalence of cyberattacks targeting IoT devices, there is a parallel rise in the development of malware across various Instruction Set Architectures ISAs. It is thus importan...
DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanisms, struggle to maintain performance under concept drift in malware domains, as their supervised learning formulation...
MAL-2025-37607 Malicious code in uim-web-sdk (npm)
The package uim-web-sdk was found to contain malicious code...
MAL-2025-7372 Malicious code in @crabas0npm/libero-earum-non (npm)
The package @crabas0npm/libero-earum-non was found to contain malicious code...
Malicious code in fabalous-login (npm)
The package fabalous-login was found to contain malicious code...
Malicious code in idig-onapp (npm)
The package idig-onapp was found to contain malicious code...
Malicious code in dyplot (npm)
The package dyplot was found to contain malicious code...
MAL-2025-19382 Malicious code in elec6ron (npm)
The package elec6ron was found to contain malicious code...
MAL-2025-22187 Malicious code in hapi-services (npm)
The package hapi-services was found to contain malicious code...
MAL-2025-37706 Malicious code in umbriel-sequelize-tool-browserify (npm)
The package umbriel-sequelize-tool-browserify was found to contain malicious code...
USN-7697-1: AIDE vulnerabilities
Rajesh Pangare discovered that AIDE incorrectly handled filenames. A local attacker could possibly use this issue to bypass the detection of malicious files. CVE-2025-54389 Rajesh Pangare discovered that AIDE incorrectly handled extended file attributes. A local attacker could possibly use this...
Demystifying the Role of Rule-Based Detection in AI Systems for Windows Malware Detection
Malware detection increasingly relies on AI systems that integrate signature-based detection with machine learning. However, these components are typically developed and combined in isolation, missing opportunities to reduce data complexity and strengthen defenses against adversarial EXEmples,...
Explainable Ensemble Learning for Graph-Based Malware Detection
Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive techniques. Graph neural networks GNNs have shown promise in this domain by modeling rich structural dependencies in graph-based program representations such a...
MalFlows: Context-Aware Fusion of Heterogeneous Flow Semantics for Android Malware Detection
Static analysis, a fundamental technique in Android app examination, enables the extraction of control flows, data flows, and inter-component communications ICCs, all of which are essential for malware detection. However, existing methods struggle to leverage the semantic complementarity across...