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Muhammad Azhar Prabukusumo
Universitas Pertahanan Republik 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.
Distributed denial of service attack prediction using a hybrid CNN GRU model on defense network infrastructure Muhammad Azhar Prabukusumo; Marthen Doga; Yulianus Kaisiepo
Journal of Defense Technology and Engineering Vol. 2 No. 1 (2026): July, Journal of Defense Technology and Engineering
Publisher : Fakultas Teknik dan Teknologi Pertahanan, Universitas Pertahanan Republik Indonesia

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Abstract

Distributed Denial of Service (DDoS) attacks pose a serious threat to defense network infrastructure by disrupting communication services that are critical for command-and-control operations. Existing detection methods often struggle to simultaneously capture the spatial characteristics of network traffic and the temporal evolution of attack patterns, limiting their effectiveness against diverse DDoS attacks. This study proposes a hybrid CNN-GRU model that integrates one-dimensional convolutional neural networks for local feature extraction with gated recurrent units for temporal dependency modeling, enabling more comprehensive representation learning than single-model approaches. The proposed framework was evaluated on a five-class subset of the CIC-DDoS2019 dataset containing 225,000 network flow records and compared with standalone CNN, GRU, and LSTM models under identical experimental settings. Experimental results demonstrate that the proposed CNN-GRU achieved the best performance, obtaining an accuracy of 0.9707 and a macro-F1 score of 0.9707, consistently outperforming all baseline models. The novelty of this study lies in the effective integration of complementary spatial and temporal learning mechanisms for multiclass DDoS attack detection, providing a more robust classification framework for defense-oriented network traffic analysis. These findings indicate that the proposed model offers a practical solution for intelligent intrusion detection and early warning systems, supporting resilient and secure defense network operations against evolving DDoS threats. Future work will extend the model to the complete twelve-class attack taxonomy and investigate attention mechanisms to further improve discrimination among closely related attack categories.
Co-Authors Baringin Sianipar Jontinus Manullang Marthen Doga Yulianus Kaisiepo