Malware Detection through Memory Analysis

This paper summarizes the research conducted for a malware detection project using the Canadian Institute for Cybersecurity's MalMemAnalysis-2022 dataset. The purpose of the project was to explore the effectiveness and efficiency of machine learning techniques for the task of binary classificatio...

Benchmarking Machine Learning Models for IoT Malware Detection under Data Scarcity and Drift

The rapid expansion of the Internet of Things IoT in domains such as smart cities, transportation, and industrial systems has heightened the urgency of addressing their security vulnerabilities. IoT devices often operate under limited computational resources, lack robust physical safeguards, and...

CAFE-GB: Scalable and Stable Feature Selection for Malware Detection Via Chunk-Wise Aggregated Gradient Boosting

High-dimensional malware datasets often exhibit feature redundancy, instability, and scalability limitations, which hinder the effectiveness and interpretability of machine learning-based malware detection systems. Although feature selection is commonly employed to mitigate these issues, many...

Malware Detection Based on API Calls: A Reproducibility Study

This study independently reproduces the malware detection methodology presented by Felli cious et al. 7, which employs order-invariant API call frequency analysis using Random Forest classification. We utilized the original public dataset 250,533 training samples, 83,511 test samples and replicat...

AI-Powered Algorithms for the Prevention and Detection of Computer Malware Infections

The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems t...

Malicious Package

Overview ing-feat-malware-detection is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this...

MeLeMaD: Adaptive Malware Detection Via Chunk-Wise Feature Selection and Meta-Learning

Confronting the substantial challenges of malware detection in cybersecurity necessitates solutions that are both robust and adaptable to the ever-evolving threat environment. The paper introduces Meta Learning Malware Detection MeLeMaD, a novel framework leveraging the adaptability and...

EUVD-2025-205479

Malicious code in ing-feat-malware-detection npm...

MAL-2025-192949 Malicious code in ing-feat-malware-detection (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 5339c05fd7459125a98a0503401e5eb8bca30c456d9a80f14485484df6256850 The package ing-feat-malware-detection was found to contain malicious code. Source: ghsa-malware...

RealDefense SUPERAntiSpyware 安全漏洞

RealDefense SUPERAntiSpyware is a security tool for detecting and removing malware from RealDefense USA. A security vulnerability exists in RealDefense SUPERAntiSpyware that stems from SAS Core Service exposing dangerous functions that could lead to local elevation of privilege...

Enhancing Decision-Making in Windows PE Malware Classification during Dataset Shifts with Uncertainty Estimation

Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable PE malware, but their reliability often degrades under dataset shifts, leading to misclassifications with severe security consequences. To address this, we enhance an existing LightGBM...

MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-Of-Distribution Malware Detection and Classification

Out of distribution OOD detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep learning based malware detectors rely on closed world assumptions and fail to...

ByteShield: Adversarially Robust End-To-End Malware Detection through Byte Masking

Research has proven that end-to-end malware detectors are vulnerable to adversarial attacks. In response, the research community has proposed defenses based on randomized and derandomized smoothing. However, these techniques remain susceptible to attacks that insert large adversarial payloads. To...

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,...