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All Journal Jurnal Simetris Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Innovation Studies Jurnal Algoritma Moneter : Jurnal Keuangan dan Perbankan Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
Chaerul Umam
Jurusan Teknik Kimia, Universitas Sultan Ageng Tirtayasa, Cilegon-Banten, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Other

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Comparative Analysis 0f Random Forest and Xgboost Performance for Network Flow Based Malware Classification Wicaksana, Fajar Adji; Umam, Chaerul
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/8f891c76

Abstract

The evolving complexity of cyber threats, particularly malware propagation through network infrastructure, necessitates intrusion detection mechanisms that are both precise and computationally efficient. This study presents an in-depth comparative analysis of two ensemble learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), in classifying network traffic anomalies based on network flow features. Empirical validation was conducted using the CSE-CIC-IDS2018 dataset, which comprehensively represents a spectrum of modern attacks. The research methodology systematically includes data preprocessing, handling class imbalance via weighting techniques, and performance evaluation based on accuracy, F1-score, and inference time metrics. Experimental results indicate that both models achieved high performance convergence with perfect Area Under Curve (AUC) scores. However, XGBoost demonstrated technical superiority with an accuracy of 99.8%, slightly surpassing Random Forest at 99.4%. The most significant finding of this study lies in computational efficiency, where XGBoost proved to be 14% faster (6.36 seconds) in prediction compared to Random Forest (7.42 seconds) on a large-scale test set. This fact confirms that the boosting architecture in XGBoost offers an optimal balance between detection sensitivity and system latency. Based on this evidence, XGBoost is recommended as the best classification model for real-time intrusion detection system implementations that prioritize rapid threat response.
Comparison Of Random Forest and Neural Network for Portable Executable Malware Classification: Perbandingan Random Forest dan Neural Network pada Klasifikasi Malware Portable Executable Nugroho, Agung; Umam, Chaerul
Indonesian Journal of Innovation Studies Vol. 27 No. 1 (2026): January
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v27i1.1883

Abstract

General Background: The rapid growth of information technology has increased the complexity of cyber threats, with malware attacks posing significant risks to computer systems, particularly those based on the Windows operating system. Specific Background: Portable Executable files contain structured statistical attributes that can be utilized to distinguish malware from benign software using machine learning techniques. Knowledge Gap: Despite extensive use of machine learning in malware detection, comparative evidence using identical Portable Executable statistical features and consistent evaluation settings remains limited. Aims: This study aims to compare the classification performance of Random Forest and Neural Network models in malware detection based on Portable Executable statistical features. Results: Using the ClaMP Integrated Dataset comprising 5,184 samples and 70 static features, Random Forest achieved an accuracy, precision, recall, and F1-score of 99.14%, while the Neural Network obtained consistent scores of 98.18% across all evaluation metrics. Novelty: This research presents a direct and controlled comparison of ensemble and neural-based classifiers using identical preprocessing pipelines, default model configurations, and balanced Portable Executable datasets. Implications: The findings demonstrate that ensemble-based approaches provide stable and reliable performance for Portable Executable malware classification and offer a practical foundation for automated machine learning–based cybersecurity systems. Highlights • Random Forest Achieved The Highest Classification Scores Across All Metrics• Portable Executable Statistical Features Provided Clear Malware Separation• Ensemble Learning Demonstrated Strong Stability On Structured PE Data Keywords Malware Detection; Portable Executable; Random Forest; Neural Network; Machine Learning
Co-Authors Abdussalam Abdussalam, Abdussalam Al zami, Farrikh Alzami, Farrikh ARIANTO, EKO De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Ervikhan, Muhammad Ibnu Firman Wahyudi, Firman Guruh Fajar Shidik Heru Lestiawan Irwan, Rhedy Isinkaye, Folasade Olubusola Islam, Hussain Md Mehedul L. Budi Handoko Megantara, Rama Aria Nugroho, Agung Nur Hariyanto, Mohammad Malik Ocky Saputra, Filmada Pulung Nurtantio Andono Reza Pahlevi, Mohammad Rizky Saputra, Filmada Ocky Saputra, Maulana Bagus Saputri, Pungky Nabella Sinaga, Daurat Sinaga, Daurat Sya'bani, Hafiidh Akbar Wellia Shinta Sari Wicaksana, Fajar Adji Widyanto Utomo, Arya Wildanil Ghozi