Firewall Implementation Using Linear Rule Matching Algorithm

Firewalls have long served as the foundational defense mechanism in network security, tracing their roots to early packet-filtering technologies designed to prevent unauthorized access. Traditional firewalls, however, often struggle with limited transparency, static rule sets, and high hardware dependency, making them less suited for dynamic and educational environments. As networks grow in scale and complexity, these constraints highlight the need for more adaptable and modular firewall systems. This paper presents Firewall Master, a Python-based software firewall framework designed with modularity, transparency, and user control at its core. The system performs real-time packet inspection using a customizable rule engine that filters traffic based on IP addresses, ports, and transport layer protocols. Unmatched packets are logged for future analysis, enabling adaptive rule refinement. The lightweight architecture ensures accessibility for students, researchers, and small network environments while laying the groundwork for future integrations with AI-based anomaly detection. Experimental results show that the system effectively filtered over 74% of network traffic on first execution and adapted to logbased rule refinements with over 80% improvement in subsequent runs. The firewall achieved low latency (<10ms per packet) and performed reliably on low-resource systems, validating its efficiency and educational value.