International Journal of Science, Engineering and TechnologyAuthors: Ravi Teja Yarlagadda
Abstract: Enterprise networks rely heavily on firewalls to regulate traffic between internal and external systems, protect sensitive resources, and enforce security policies. Over time, firewall rule sets become increasingly complex, often comprising hundreds or thousands of entries. Frequent updates to accommodate new applications, compliance requirements, or infrastructure changes introduce the risk of misconfigurations that can compromise security, disrupt connectivity, or degrade network performance. Traditional approaches, such as manual audits or automated rule verification tools, typically focus on individual rule correctness but fail to capture the broader, network-wide impact of policy changes. This study presents a graph-based framework for proactive firewall policy impact analysis, representing network devices, subnets, and firewall rules as nodes and edges in a structured graph. Policy changes including additions, deletions, and modifications are applied as updates to this graph, allowing both direct and indirect effects on network connectivity, security, and performance to be systematically assessed. The framework incorporates quantitative metrics, including reachability between nodes, exposure of critical or sensitive systems, and performance implications such as path length and bottleneck detection. Additionally, visualization techniques are employed to highlight affected nodes and edges, enabling administrators to quickly identify high-risk areas and make informed decisions. A case study conducted on a representative enterprise network demonstrates the framework’s effectiveness in detecting unintended access paths, identifying critical gateway nodes, and quantifying connectivity and performance changes. Results indicate that graph-based modeling not only reduces the risk of oversight but also provides actionable insights that conventional audits may overlook. The proposed methodology is scalable, repeatable, and adaptable to large-scale networks, providing a foundation for future enhancements, including integration with automated policy management systems, predictive analytics, and extensions to dynamic, cloud, and multi-domain environments.
DOI: https://doi.org/10.5281/zenodo.18358792
