Adversarially Robust and Interpretable Magecart Malware Detection

Magecart skimming attacks have emerged as a significant threat to client-side security and user trust in online payment systems. This paper addresses the challenge of achieving robust and explainable detection of Magecart attacks through a comparative study of various Machine Learning ML models...

Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems

With the increasing frequency and sophistication of Distributed Denial of Service DDoS attacks, it has become critical to develop more efficient and interpretable detection methods. Traditional detection systems often struggle with scalability and transparency, hindering real-time response and...

WordPress OOPSpam Anti-Spam plugin IP Header Forgery Vulnerability

WordPress OOPSpam Anti-Spam plugin is an anti-spam plugin designed for WordPress that protects forms and comments from spam through AI and machine learning techniques without the use of CAPTCHA validation. The WordPress OOPSpam Anti-Spam plugin suffers from an IP header forgery vulnerability that...

SHIELD: Securing Healthcare IoT with Efficient Machine Learning Techniques for Anomaly Detection

The integration of IoT devices in healthcare introduces significant security and reliability challenges, increasing susceptibility to cyber threats and operational anomalies. This study proposes a machine learning-driven framework for 1 detecting malicious cyberattacks and 2 identifying faulty...

Trustworthy Quantum Machine Learning: A Roadmap for Reliability, Robustness, and Security in the NISQ Era

Quantum machine learning QML is a promising paradigm for tackling computational problems that challenge classical AI. Yet, the inherent probabilistic behavior of quantum mechanics, device noise in NISQ hardware, and hybrid quantum-classical execution pipelines introduce new risks that prevent...

Machine and Deep Learning for Indoor UWB Jammer Localization

Ultra-wideband UWB localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning ML and deep learning DL methods have improved tag localization, localizing...

Federated Cyber Defense: Privacy-Preserving Ransomware Detection across Distributed Systems

Detecting malware, especially ransomware, is essential to securing today's interconnected ecosystems, including cloud storage, enterprise file-sharing, and database services. Training high-performing artificial intelligence AI detectors requires diverse datasets, which are often distributed acros...

Detecting Vulnerabilities from Issue Reports for Internet-Of-Things

Timely identification of issue reports reflecting software vulnerabilities is crucial, particularly for Internet-of-Things IoT where analysis is slower than non-IoT systems. While Machine Learning ML and Large Language Models LLMs detect vulnerability-indicating issues in non-IoT systems, their I...

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...

Meta-Learning Based Radio Frequency Fingerprinting for GNSS Spoofing Detection

The rapid development of technology has led to an increase in the number of devices that rely on position, velocity, and time PVT information to perform their functions. As such, the Global Navigation Satellite Systems GNSS have been adopted as one of the most promising solutions to provide PVT...

Penetrating the Hostile: Detecting DeFi Protocol Exploits through Cross-Contract Analysis

Decentralized finance DeFi protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes...

CVE-2025-64366 WordPress MasterStudy LMS plugin <= 3.6.27 - SQL Injection vulnerability

Improper Neutralization of Special Elements used in an SQL Command 'SQL Injection' vulnerability in Stylemix MasterStudy LMS masterstudy-lms-learning-management-system allows Blind SQL Injection.This issue affects MasterStudy LMS: from n/a through = 3.6.27...

PT-2025-44617

Name of the Vulnerable Software and Affected Versions Stylemix MasterStudy LMS versions prior to 3.6.28 Description A flaw exists in Stylemix MasterStudy LMS that allows for Blind SQL Injection due to improper neutralization of special elements within SQL commands. This issue potentially allows...

WordPress plugin Masterstudy 安全漏洞

WordPress Masterstudy plugin is a free learning management system plugin designed for WordPress. The WordPress Masterstudy plugin suffers from a file inclusion vulnerability that stems from improper control over the filename of include or request statements, which can be exploited by an attacker ...

MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research

We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide range of features and extensive metadata. To ensure accurate malware classification, we employ the VirusTotal API,...

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...

On Selecting Few-Shot Examples for LLM-Based Code Vulnerability Detection

Large language models LLMs have demonstrated impressive capabilities for many coding tasks, including summarization, translation, completion, and code generation. However, detecting code vulnerabilities remains a challenging task for LLMs. An effective way to improve LLM performance is in-context...

A DRL-Empowered Multi-Level Jamming Approach for Secure Semantic Communication

Semantic communication SemCom aims to transmit only task-relevant information, thereby improving communication efficiency but also exposing semantic information to potential eavesdropping. In this paper, we propose a deep reinforcement learning DRL-empowered multi-level jamming approach to enhanc...

EUVD-2025-36706

MLflow Weak Password Requirements Authentication Bypass Vulnerability. This vulnerability allows remote attackers to bypass authentication on affected installations of MLflow. Authentication is not required to exploit this vulnerability. The specific flaw exists within the handling of passwords...

CVE-2025-64212

CVE-2025-64212 affects the WordPress MasterStudy LMS Pro plugin prior to 4.7.16. The vulnerability is a missing authorization/broken access control issue allowing exploitation due to incorrectly configured access control security levels. Affected component is the WordPress plugin MasterStudy LMS ...