AI-Powered Malware Detection System for Enhanced Cybersecurity in Small Networks

Abstract

This paper presents an AI-powered malware detection system designed specifically for enhancing cybersecurity in small network environments. The system employs machine learning algorithms to analyze network traffic and identify potential malware threats accurately and efficiently. By utilizing supervised learning techniques on labeled network data, the model detects known and emerging malware patterns, reducing false positives and improving response time. The lightweight framework is optimized for resource-constrained setups typical to small enterprises, ensuring high detection rates without compromising network performance. Experimental results demonstrate the system's effectiveness in detecting various malware types in real-world network scenarios. This study contributes a practical security solution tailored to the needs of small networks, where traditional enterprise-grade tools may be infeasible due to cost or complexity.