<![CDATA[The escalating complexity of computer networks and cybersecurity threats demand analytical approaches capable of systematically and measurably representing network structure. Discrete mathematical graph theory offers a formal framework for modeling network topology as nodes and edges, thus potentially supporting more effective intrusion detection and firewall placement optimization. This research aims to conduct a Systematic Literature Review of publications from 2022–2026 to identify graph theory applications in intrusion detection, evaluate the most effective graph-based firewall optimization methods, and map research gaps and future development trends. The methodology employed follows the SLR protocol with stages of systematic search across reputable databases, selection based on inclusion-exclusion criteria, and analysis through descriptive-comparative meta-analysis, thematic meta-synthesis, and content analysis. Results show the dominance of weighted graphs and structure-based learning approaches for network anomaly detection, as well as firewall optimization modeling through integer linear programming and graph heuristics. This research contributes to presenting an integrated synthesis between intrusion detection and firewall optimization within discrete graph framework, and provides conceptual foundation for developing adaptive network security models based on mathematical structure.]]>
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