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All Journal JITU : Journal Informatic Technology And Communication Technema: Journal of Intelligent Engineering and Computing
Andhika Adnan
Universitas Widya Dharma Pontianak
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science

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Network Intrusion Detection Using Ensemble Learning Techniques on the CIC-IDS2017 Public Dataset Sugeng Hendra Wijaya; Andhika Adnan
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 1 (2026): March: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Network intrusion detection remains a critical challenge in cybersecurity due to evolving attack patterns, class imbalance, and high-dimensional network traffic data. This study investigates the effectiveness of ensemble learning techniques on the CIC-IDS2017 public dataset, integrating decision trees, random forests, and gradient boosting models through stacking, voting, and hybrid Boost-Bag strategies. Data preprocessing involved normalization, handling missing values, and feature selection based on correlation and mutual information to reduce dimensionality while preserving predictive relevance. Empirical evaluation employed stratified 10-fold cross-validation and performance metrics including accuracy, recall, F1-score, and AUC-ROC, with additional analyses of confusion matrices and temporal stability to assess operational reliability. Results indicate that hybrid ensembles achieve superior detection performance, particularly for low-frequency attacks, while maintaining moderate computational overhead compared to individual classifiers. Comparative insights reveal trade-offs between accuracy, minority-class sensitivity, and inference latency, guiding practical deployment considerations. The findings substantiate the theoretical benefits of ensemble diversity and optimized feature selection, offering a robust framework for scalable, interpretable, and resilient network intrusion detection systems.
Teori Graf Diskrit untuk Deteksi Intrusi dan Optimasi Firewall: Systematic Literature Review Andhika Adnan; Fransiskus Mario Hartono Tjiptabudi; Ricky Imanuel Ndaumanu; Yohanis Malelak
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2306

Abstract

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.
Co-Authors Ricky Imanuel Ndaumanu Sugeng Hendra Wijaya Tjiptabudi, Fransiskus Mario Hartono Yohanis Malelak