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Jonson Manurung
Universtias Pertahanan Republik Indonesia, Bogor, 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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Recurrent neural network for adaptive cyber attack prediction on critical defense systems Jonson Manurung; Hengki Tamando Sihotang
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

The threat of cyber attacks against critical defense systems is becoming increasingly complex and dynamic, requiring adaptive and proactive prediction mechanisms. This study aims to develop a Recurrent Neural Network (RNN) model to predict cyber attacks on critical defense systems with high accuracy and generalization capabilities against new attacks. The CICIDS2020 dataset was used to train and test the model, with 70% of the data allocated for training, 15% for validation, and 15% for testing. The RNN architecture was optimized by selecting the number of hidden layers, the number of neurons per layer, the activation function, and the application of dropout and regularization to minimize the risk of overfitting. The model was trained using the Backpropagation Through Time (BPTT) algorithm and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The results show that RNN outperforms LSTM, Random Forest, and SVM algorithms, with an accuracy of 97.8%, precision of 96.5%, recall of 95.9%, F1-score of 96.2%, and AUC of 0.981, and is capable of detecting rare attacks. These findings confirm the effectiveness of RNN in capturing long-term temporal patterns in cyberattack data and providing adaptive predictions for new attacks. The practical implications of this research include strengthening critical defense systems through early detection and real-time mitigation of cyberattacks, as well as providing a basis for the development of reliable proactive security systems.
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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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 Baringin Sianipar Hengki Tamando Sihotang