TOPIC

malware

Aggregated news, research, and updates specifically regarding malware. Auto-curated by our AI Engine.

Research #adversarial attacks 🔬 ResearchAnalyzed: Jan 10, 2026 07:31

Adversarial Attacks on Android Malware Detection via LLMs

Published:Dec 24, 2025 19:56

•

1 min read

•

ArXiv

Analysis

This research explores the vulnerability of Android malware detectors to adversarial attacks generated by Large Language Models (LLMs). The study highlights a concerning trend where sophisticated AI models are being leveraged to undermine the security of existing systems.

Key Takeaways

•LLMs can be used to craft adversarial examples that evade Android malware detectors.
•The attacks operate at the feature level, potentially making them more subtle and difficult to detect.
•This research highlights a new threat vector in mobile security.

Reference

“The research focuses on LLM-driven feature-level adversarial attacks.”

Permalink ArXiv

Research #Malware 🔬 ResearchAnalyzed: Jan 10, 2026 07:51

pokiSEC: A Scalable, Containerized Sandbox for Malware Analysis

Published:Dec 24, 2025 00:38

•

1 min read

•

ArXiv

Analysis

The article introduces pokiSEC, a novel approach to malware analysis utilizing a multi-architecture, containerized sandbox. This architecture potentially offers improved scalability and agility compared to traditional sandbox solutions.

Key Takeaways

•Focuses on containerized environments for malware analysis.
•Supports multiple architectures for comprehensive testing.
•Employs ephemeral sandboxes for rapid analysis and disposal.

Reference

“pokiSEC is a Multi-Architecture, Containerized Ephemeral Malware Detonation Sandbox.”

Permalink ArXiv

Research #Malware 🔬 ResearchAnalyzed: Jan 10, 2026 09:07

Improving Malware Classification with Uncertainty Estimation in Shifting Datasets

Published:Dec 20, 2025 20:17

•

1 min read

•

ArXiv

Analysis

This research explores a crucial area of cybersecurity, addressing the challenge of accurate malware classification, particularly when datasets evolve. The focus on uncertainty estimation is a valuable approach for improving the reliability and robustness of machine learning models in dynamic environments.

Key Takeaways

•Addresses the problem of dataset shifts in malware classification.
•Employs uncertainty estimation techniques.
•Aims to improve decision-making in classifying Windows PE malware.

Reference

“The research focuses on Windows PE malware classification.”

Permalink ArXiv

Research #Malware 🔬 ResearchAnalyzed: Jan 10, 2026 09:33

MAD-OOD: Deep Learning Framework for Out-of-Distribution Malware Detection

Published:Dec 19, 2025 14:02

•

1 min read

•

ArXiv

Analysis

The paper introduces MAD-OOD, a deep learning framework designed to detect and classify malware that falls outside of the training distribution. This is a significant contribution to cybersecurity, as it addresses the challenge of identifying novel or evolving malware threats.

Key Takeaways

Reference

“MAD-OOD is a deep learning cluster-driven framework for out-of-distribution malware detection and classification.”

Permalink ArXiv

Research #Cloud Security 🔬 ResearchAnalyzed: Jan 10, 2026 10:36

AI-Powered Cloud Security: A Fusion-Based Approach to Malware and Anomaly Detection

Published:Dec 16, 2025 21:56

•

1 min read

•

ArXiv

Analysis

This ArXiv article presents a research-focused application of AI in cloud security, specifically targeting malware and anomalous log behavior detection using a fusion-based approach within an AI-driven Security Operations Center (AISOC). The research suggests a novel method for improving cloud security posture; however, the practicality and real-world performance require further evaluation.

Key Takeaways

•The research explores the use of AI within a Security Operations Center for improved cloud security.
•The focus is on detecting both malware and anomalous log behavior using a fusion approach.
•The paper is likely to describe the architecture and evaluation of the proposed fusion-based system.

Reference

“The article's context focuses on a fusion-based AISOC for malware and log behavior detection.”

Permalink ArXiv

Research #Malware 🔬 ResearchAnalyzed: Jan 10, 2026 10:51

UIXPOSE: Novel Malware Detection on Mobile Platforms

Published:Dec 16, 2025 06:26

•

1 min read

•

ArXiv

Analysis

This research explores a new method for detecting mobile malware by analyzing discrepancies between a program's intended behavior and its actual actions. The paper's novelty lies in its application of intention-behavior discrepancy analysis to the domain of mobile security, offering a potential advancement in malware detection techniques.

Key Takeaways

•The research focuses on mobile malware detection.
•It uses intention-behavior discrepancy analysis.
•The findings are from an ArXiv publication.

Reference

“UIXPOSE utilizes intention-behaviour discrepancy analysis for mobile malware detection.”

Permalink ArXiv

Research #LLMs 🔬 ResearchAnalyzed: Jan 10, 2026 11:33

Detecting Malicious NPM Packages with Taint-Based Code Slicing and LLMs

Published:Dec 13, 2025 12:56

•

1 min read

•

ArXiv

Analysis

This ArXiv paper explores a novel approach to identify malicious NPM packages using taint-based code slicing and Large Language Models. The integration of these techniques shows promise in enhancing software supply chain security.

Key Takeaways

•Applies taint-based code slicing to analyze NPM packages.
•Leverages Large Language Models for malicious code detection.
•Addresses the growing concern of software supply chain attacks.

Reference

“The research focuses on using taint-based code slicing for the detection of malicious NPM packages.”

Permalink ArXiv

Research #Malware 🔬 ResearchAnalyzed: Jan 10, 2026 12:21

K-Means for Malware Clustering: A Comparative Analysis

Published:Dec 10, 2025 11:24

•

1 min read

•

ArXiv

Analysis

This research paper from ArXiv analyzes the application of K-Means clustering for malware identification based on hash values, offering a comparative perspective. The study likely explores the effectiveness of K-Means in grouping similar malware families and its practical implications for cybersecurity.

Key Takeaways

•Applies K-Means to cluster malware based on hash values.
•The analysis is comparative, potentially evaluating different K-Means configurations.
•Implies practical applications for malware detection and analysis.

Reference

“The research focuses on hash-based malware clustering using K-Means.”

Permalink ArXiv

Loading topic feed...

malware

Adversarial Attacks on Android Malware Detection via LLMs

Analysis

Key Takeaways

pokiSEC: A Scalable, Containerized Sandbox for Malware Analysis

Analysis

Key Takeaways

Improving Malware Classification with Uncertainty Estimation in Shifting Datasets

Analysis

Key Takeaways

MAD-OOD: Deep Learning Framework for Out-of-Distribution Malware Detection

Analysis

Key Takeaways

AI-Powered Cloud Security: A Fusion-Based Approach to Malware and Anomaly Detection

Analysis

Key Takeaways

UIXPOSE: Novel Malware Detection on Mobile Platforms

Analysis

Key Takeaways

Detecting Malicious NPM Packages with Taint-Based Code Slicing and LLMs

Analysis

Key Takeaways

K-Means for Malware Clustering: A Comparative Analysis

Analysis

Key Takeaways

📬 Get AI News Delivered

Browse by Category

Trending Topics

Adversarial Attacks on Android Malware Detection via LLMs

Analysis

Key Takeaways

pokiSEC: A Scalable, Containerized Sandbox for Malware Analysis

Analysis

Key Takeaways

Improving Malware Classification with Uncertainty Estimation in Shifting Datasets

Analysis

Key Takeaways

MAD-OOD: Deep Learning Framework for Out-of-Distribution Malware Detection

Analysis

Key Takeaways

AI-Powered Cloud Security: A Fusion-Based Approach to Malware and Anomaly Detection

Analysis

Key Takeaways

UIXPOSE: Novel Malware Detection on Mobile Platforms

Analysis

Key Takeaways

Detecting Malicious NPM Packages with Taint-Based Code Slicing and LLMs

Analysis

Key Takeaways

K-Means for Malware Clustering: A Comparative Analysis

Analysis

Key Takeaways

📬 Get AI News Delivered

Browse by Category

Trending Topics