A Comparative Study of Machine Learning Algorithms for Intrusion Detection Systems using the NSL-KDD Dataset

Rulyansyah Permata Putra, Amarudin Amarudin

Abstract


In today’s digital era, cyberattacks are becoming increasingly complex, rendering traditional rule-based Intrusion Detection Systems (IDS) often ineffective in recognizing new attack patterns. The primary objective of this study is to design and implement a machine learning model for detecting network intrusions efficiently while minimizing latency, through a comparative analysis of several algorithms: Decision Tree, Random Forest, Support Vector Machine (SVM), and Boosting. The research methodology includes the collection of the NSL-KDD dataset, followed by data transformation, cleaning, normalization, and partitioning into training and testing sets. Each algorithm was trained using tuned parameters, and performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and an analysis of training and prediction time. The results indicate that the Boosting algorithm stands out, achieving an accuracy rate of 99.36%. Boosting also demonstrated greater reliability in detecting minority classes, despite requiring longer training times. The application of machine learning methods—particularly Boosting—proves to be an effective approach to enhancing intrusion detection and can serve as a foundation for developing more adaptive and reliable cybersecurity systems.

Keywords


Intrusion Detection System; Boosting; Machine Learning; NSL-KDD; Keamanan Siber

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References


B. Dhanunjay, E. Sanjay, A. K. Raj, and M. Dholvan, “Intrusion Detection System using Machine Learning,” Quantum Comput. Model. Cybersecurity Wirel. Commun., Vol. 6, No. 3, pp. 279–292, 2020, doi: 10.1002/9781394271429.ch19.

P. Dini, A. Elhanashi, A. Begni, S. Saponara, Q. Zheng, and K. Gasmi, “Overview on Intrusion Detection Systems Design Exploiting Machine Learning for Networking Cybersecurity,” Appl. Sci., Vol. 13, No. 13, 2023, doi: 10.3390/app13137507.

Amarudin, Styawati, Syaifuddin, and M. Iqbal, “Improving Intrusion Detection System on Servers using Machine Learning-Based RFERF Technique,” Proceeding 2024 9th Int. Conf. Inf. Technol. Digit. Appl. ICITDA 2024, pp. 1–8, 2024, doi: 10.1109/ICITDA64560.2024.10809789.

K. N. Jaya Varshini R, Sifa Thahasin F, Jayasri S, “Intrusion Detection System using Machine Learning Algorithm,” Quantum Comput. Model. Cybersecurity Wirel. Commun., pp. 279–292, 2023, doi: 10.1002/9781394271429.ch19.

A. Efe and İ. N. Abaci, “Comparison of the Host based Intrusion Detection Systems and Network Based Intrusion Detection Systems,” Celal Bayar Üniversitesi Fen Bilim. Derg., Vol. 18, No. 1, pp. 23–32, 2022, doi: 10.18466/cbayarfbe.832533.

A. Elhanashi, K. Gasmi, A. Begni, P. Dini, Q. Zheng, and S. Saponara, “Machine Learning Techniques for Anomaly-based Detection System on CSE-CIC-IDS2018 Dataset,” Lect. Notes Electr. Eng., vol. 1036 LNEE, No. April, pp. 131–140, 2023, doi: 10.1007/978-3-031-30333-3_17.

Amarudin, R. Ferdiana, and Widyawan, “B-DT Model: A Derivative Ensemble Method to Improve Performance of Intrusion Detection System,” J. Adv. Inf. Technol., Vol. 15, No. 1, pp. 87–103, 2024, doi: 10.12720/jait.15.1.87-103.

A. Lama and D. P. Savant, “Network-based Intrusion Detection Systems using Machine Learning Algorithms,” Int. J. Eng. Appl. Sci. Technol., Vol. 6, No. 11, pp. 145–155, 2022, doi: 10.33564/ijeast.2022.v06i11.028.

O. D. Okey et al., “BoostedEnML: Efficient Technique for Detecting Cyberattacks in IoT Systems using Boosted Ensemble Machine Learning,” Sensors, Vol. 22, No. 19, pp. 1–26, 2022, doi: 10.3390/s22197409.

A. Shahraki, M. Abbasi, and Ø. Haugen, “Boosting Algorithms for Network Intrusion Detection: A Comparative Evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost,” Eng. Appl. Artif. Intell., Vol. 94, No. February, p. 103770, 2020, doi: 10.1016/j.engappai.2020.103770.

M. Hernowo and E. Sugiharti, “XGBoost Algorithm on Intrusion Detection System in Detecting Network Intrusions,” Endang Sugiharti Innov. J. Soc. Sci. Res., Vol. 4, pp. 10572–10588, 2024.

A. M. Sani, A. S. Ben-musa, and M. Haladu, “In-Depth Study of Decision Tree Model,” Int. J. SCI. Res., Vol. 10, No. 11, pp. 705–709, 2021, doi: 10.21275/MR211102051237.

D. P. Sari, Z. Halim, Irlon, B. Waseso, and Saromah, “Implementasi Machine Learning untuk Deteksi Intrusi pada Jaringan Komputer,” Vol. 13, No. September, pp. 1389–1394, 2024.

T. Zhu, “Analysis on the Applicability of the Random Forest,” J. Phys. Conf. Ser., Vol. 1607, No. 1, 2020, doi: 10.1088/1742-6596/1607/1/012123.

M. Aljanabi, M. A. Ismail, and A. H. Ali, “Intrusion Detection Systems, Issues, Challenges, and Needs,” Int. J. Comput. Intell. Syst., Vol. 14, No. 1, pp. 560–571, 2021, doi: 10.2991/ijcis.d.210105.001.

M. Farooq, M. H. Khan, and R. A. Khan, “Dynamic Threat Landscape Analysis and Adaptive Response Strategies for Intrusion Detection and Prevention Systems using Advance Gradient Boosting Algorithms,” Ijarcce, Vol. 13, No. 3, pp. 251–264, 2024, doi: 10.17148/ijarcce.2024.13243.




DOI: https://doi.org/10.32520/stmsi.v14i4.5246

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