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All Journal Journal of Defense Technology and Engineering
Baringin Sianipar
Universitas HKBP Nomensen, Medan, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Particle Swarm Optimization for Multi Objective Optimization of Intrusion Detection in National Defense Cyber Infrastructure Muhammad Azhar Prabukusumo; Jontinus Manullang; Baringin Sianipar
Journal of Defense Technology and Engineering Vol. 1 No. 1 (2025): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

Cybersecurity is a critical component of national defense, yet conventional Intrusion Detection Systems (IDS) often face limitations such as high false positive rates, detection delays, and difficulty adapting to dynamic attack patterns, leading to potential blind spots in defense networks. This study aims to design an adaptive IDS that balances detection accuracy, false positives, and operational efficiency through the application of multi objective Particle Swarm Optimization (PSO). Using the CICIDS2017 dataset, which simulates realistic modern network traffic and attack scenarios, we developed and evaluated a PSO optimized IDS model. The experimental methodology included preprocessing, feature selection, model training, and optimization of key performance objectives—maximizing detection rate (DR), minimizing false positive rate (FPR), and reducing latency. The results demonstrate that the proposed PSO IDS achieved a detection rate of 0.96 compared to 0.85 in conventional IDS, reduced the false positive rate from 0.18 to 0.07, and lowered average detection latency from 0.35 seconds to 0.12 seconds. Pareto front analysis confirmed that the multi objective optimization effectively balances conflicting parameters, delivering more robust and resilient intrusion detection. These findings indicate that PSO based multi objective IDS can serve as a practical and scalable solution for strengthening national cyber defense infrastructures, while also providing policy relevant insights on the integration of AI driven optimization methods into defense strategies.
Enhanced cyber attack detection using optimized random forest with SMOTE-based class balancing and feature selection Jonson Manurung; Adam Mardamsyah; Baringin Sianipar
Journal of Defense Technology and Engineering Vol. 1 No. 2 (2026): January, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

The rapid expansion of interconnected enterprise networks has intensified cybersecurity threats, while traditional signature-based intrusion detection systems remain ineffective against evolving and imbalanced attack patterns, particularly zero-day and low-frequency attacks. This study aims to develop an optimized and practically deployable intrusion detection framework by leveraging a Random Forest classifier on the CIC-IDS2017 benchmark dataset, with emphasis on robust minority attack detection, computational efficiency, and interpretability for real-world security operations. The proposed method integrates comprehensive data preprocessing, Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance mitigation, feature importance–driven dimensionality reduction, and exhaustive grid search–based hyperparameter optimization within a unified machine learning pipeline. Experiments conducted on 2.52 million network flow records demonstrate that the optimized model achieves 98.14% accuracy, 96.25% weighted F1-score, and 0.993 ROC-AUC, while maintaining stable performance across all attack categories, including minority classes such as Infiltration and Botnet with F1-scores exceeding 93%. Feature selection reduced dimensionality by 58.3% and training time by 63.2% without degrading performance, enhancing deployment feasibility in enterprise intrusion detection environments. Comparative analysis confirms that the proposed approach outperforms baseline Random Forest models, traditional machine learning methods, and recent deep learning approaches while requiring significantly lower computational resources. These findings indicate that a holistically optimized Random Forest framework offers a reliable, interpretable, and operationally efficient solution for real-world network security monitoring and cyber defense systems.
Co-Authors Adam Mardamsyah Jonson Manurung Jontinus Manullang Muhammad Azhar Prabukusumo