1109 matches found
A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation
Graph Neural Networks GNNs have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings togeth...
Improving the Identification of Real-World Malware's DNS Covert Channels Using Locality Sensitive Hashing
Nowadays, malware increasingly uses DNS-based covert channels in order to evade detection and maintain stealthy communication with its command-and-control servers. While prior work has focused on detecting such activity, identifying specific malware families and their behaviors from captured...
Accuracy and Efficiency Trade-Offs in LLM-Based Malware Detection and Explanation: A Comparative Study of Parameter Tuning Vs. Full Fine-Tuning
This study examines whether Low-Rank Adaptation LoRA fine-tuned Large Language Models LLMs can approximate the performance of fully fine-tuned models in generating human-interpretable decisions and explanations for malware classification. Achieving trustworthy malware detection, particularly when...
LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection
Machine learning ML-based malware detectors degrade over time as concept drift introduces new and evolving families unseen during training. Retraining is limited by the cost and time of manual labeling or sandbox analysis. Existing approaches mitigate this via drift detection and selective...
Retrofit: Continual Learning with Bounded Forgetting for Security Applications
Modern security analytics are increasingly powered by deep learning models, but their performance often degrades as threat landscapes evolve and data representations shift. While continual learning CL offers a promising paradigm to maintain model effectiveness, many approaches rely on full...
Malicious code in alphard-cluster-reveal-md-sedna (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector ee22bbbe9632215a2c3c8b8578c6322a0cfb2caa0be027efb0af754d7e2e82ec This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
MAL-2025-85099 Malicious code in erick-lepet78-sluey (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 31be46a1e49eaa3e3d46b9734d4195f62fc0a6f4047d1fb301a42f0a4274611a This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection
High-quality data scarcity hinders malware detection, limiting ML performance. We introduce MalDataGen, an open-source modular framework for generating high-fidelity synthetic tabular data using modular deep learning models e.g., WGAN-GP, VQ-VAE. Evaluated via dual validation TR-TS/TS-TR, seven...
CVE-2025-61303
Hatching Triage Sandbox Windows 10 build 2004 2025-08-14 and Windows 10 LTSC 20212025-08-14 contains a vulnerability in its Windows behavioral analysis engine that allows a submitted malware sample to evade detection and cause denial-of-analysis. The vulnerability is triggered when a sample...
EUVD-2008-5514
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EUVD-2009-1777
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EUVD-2008-5499
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EUVD-2012-1454
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EUVD-2012-1457
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EUVD-2012-0038
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EUVD-2012-1461
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EUVD-2008-0917
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EUVD-2012-1452
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