Volume 17, Number 3
Access Detection System on Web Cameras and Microphones
Authors
Homam El-Taj, Amena Khoja, Basmah Alsharif, Dana Alsulami and Jumaina Abdulmajed, Dar Al-Hekma University, Saudi Arabia
Abstract
The proliferation of unauthorized access to webcams and microphones poses significant risks to user privacy and security. Current tools in the market fail to provide comprehensive detection and prevention mechanisms, often lacking hybrid capabilities and quick responses. SilentEye is a proposed system designed to address these gaps by integrating active monitoring, a database-driven detection approach, and automated mitigation features. Built for Windows PCs, SilentEye combines PowerShell scripts, Python-based interfaces, and MySQL databases to detect and respond to both known and unknown threats. This paper outlines the architecture, workflow, and proposed functionality of SilentEye, highlighting its ability to disable compromised devices, notify users of unauthorized access, and adapt dynamically through its evolving database. Though currently limited in platform scope and attack coverage, SilentEye lays the groundwork for a scalable, AI-enhanced detection framework. By addressing critical vulnerabilities and setting new standards for privacy protection, SilentEye demonstrates its potential as a robust tool in the evolving landscape of cybersecurity. Future work will focus on testing, cross-platform expansion, and integration of advanced machine learning techniques to further enhance its capabilities.
Keywords
Unauthorized access detection, Webcam security, Microphone security, PowerShell.