Taint-Based Code Slicing for LLMs-Based Malicious NPM Package Detection

The increasing sophistication of malware attacks in the npm ecosystem, characterized by obfuscation and complex logic, necessitates advanced detection methods. Recently, researchers have turned their attention from traditional detection approaches to Large Language Models LLMs due to their strong...

UBUNTU-CVE-2025-40218

In the Linux kernel, the following vulnerability has been resolved: mm/damon/vaddr: do not repeat pteoffsetmaplock until success DAMON's virtual address space operation set implementation vaddr calls pteoffsetmaplock inside the page table walk callback function. This is for reading and writing pa...

Secure Over-The-Air Computation against Multiple Eavesdroppers Using Correlated Artificial Noise

In the era of the Internet of Things and massive connectivity, many engineering applications, such as sensor fusion and federated edge learning, rely on efficient data aggregation from geographically distributed users over wireless networks. Over-the-air computation shows promising potential for...

Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection

Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models LLMs in specialized tasks. However, its effectiveness depends heavily on the selection and quality of in-context examples, particularly in complex domains. In this wor...

An Efficient Privacy-Preserving Intrusion Detection Scheme for UAV Swarm Networks

The rapid proliferation of unmanned aerial vehicles UAVs and their applications in diverse domains, such as surveillance, disaster management, agriculture, and defense, have revolutionized modern technology. While the potential benefits of swarm-based UAV networks are growing significantly, they...

Small Language Models for Phishing Website Detection: Cost, Performance, and Privacy Trade-Offs

Phishing websites pose a major cybersecurity threat, exploiting unsuspecting users and causing significant financial and organisational harm. Traditional machine learning approaches for phishing detection often require extensive feature engineering, continuous retraining, and costly infrastructur...

Randomized Controlled Trials for Phishing Triage Agent

Security operations centers SOCs face a persistent challenge: efficiently triaging a high volume of user-reported phishing emails while maintaining robust protection against threats. This paper presents the first randomized controlled trial RCT evaluating the impact of a domain-specific AI agent ...

Adaptive Dual-Layer Web Application Firewall (ADL-WAF) Leveraging Machine Learning for Enhanced Anomaly and Threat Detection

Web Application Firewalls are crucial for protecting web applications against a wide range of cyber threats. Traditional Web Application Firewalls often struggle to effectively distinguish between malicious and legitimate traffic, leading to limited efficacy in threat detection. To overcome these...

Decoupling Bias, Aligning Distributions: Synergistic Fairness Optimization for Deepfake Detection

Fairness is a core element in the trustworthy deployment of deepfake detection models, especially in the field of digital identity security. Biases in detection models toward different demographic groups, such as gender and race, may lead to systemic misjudgments, exacerbating the digital divide...

Enhancing Password Security through a High-Accuracy Scoring Framework Using Random Forests

Password security plays a crucial role in cybersecurity, yet traditional password strength meters, which rely on static rules like character-type requirements, often fail. Such methods are easily bypassed by common password patterns e.g., 'P@ssw0rd1!', giving users a false sense of security. To...

Smartphone User Fingerprinting on Wireless Traffic

Due to the openness of the wireless medium, smartphone users are susceptible to user privacy attacks, where user privacy information is inferred from encrypted Wi-Fi wireless traffic. Existing attacks are limited to recognizing mobile apps and their actions and cannot infer the smartphone user...

Android Malware Detection: A Machine Learning Approach

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and...

An In-Depth Analysis of Cyber Attacks in Secured Platforms

There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue in mobile communication, disrupting user experiences and...

Active Localization of Close-Range Adversarial Acoustic Sources for Underwater Data Center Surveillance

Underwater data infrastructures offer natural cooling and enhanced physical security compared to terrestrial facilities, but are susceptible to acoustic injection attacks that can disrupt data integrity and availability. This work presents a comprehensive surveillance framework for localizing and...

Virtuozzo Hybrid Infrastructure 6.2 Update 1 Hotfix 7 (6.2.1-97)

This update provides stability fixes and support for cumulative updates. Clusters running version 6.2.1 or later can now upgrade directly to the latest available version in a single step. Vulnerability id: VSTOR-91715 Failed to retry updating the kernel. Vulnerability id: VSTOR-93149, VSTOR-10082...

Bridging Semantics and Structure for Software Vulnerability Detection Using Hybrid Network Models

Software vulnerabilities remain a persistent risk, yet static and dynamic analyses often overlook structural dependencies that shape insecure behaviors. Viewing programs as heterogeneous graphs, we capture control- and data-flow relations as complex interaction networks. Our hybrid framework...

EUVD-2019-11896

Malware in sbrugna...

EUVD-2019-2297

Malware in sbrugna...

EUVD-2021-20809

Malware in sbrugna...

Real-VulLLM: An LLM Based Assessment Framework in the Wild

Artificial Intelligence AI and more specifically Large Language Models LLMs have demonstrated exceptional progress in multiple areas including software engineering, however, their capability for vulnerability detection in the wild scenario and its corresponding reasoning remains underexplored...