(Universitas Ahmad Dahlan, Indonesia)(2) Sri Winiarti
(Informatics Department, Universitas Ahmad Dahlan, Indonesia)(3) Herman Yuliansyah
(Informatics Department, Universitas Ahmad Dahlan, Indonesia)(4) Muhammad ‘Arif Bin Mohamad
(Universiti Malaysia Pahang Al-Sultan Abdullah, Malaysia)*corresponding author
AbstractCyberattacks are becoming increasingly sophisticated, necessitating defense mechanisms that go beyond simple detection to include severity assessment for prioritizing mitigation. This study proposes a comprehensive machine learning framework to classify cyberattack severity levels (Low, Medium, High) using a modern, high-dimensional dataset. Addressing the critical challenge of class imbalance, the research integrates the Synthetic Minority Oversampling Technique (SMOTE) with a rigorous feature selection process involving SelectKBest. Four algorithms Naive Bayes, K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were evaluated using 10-fold cross-validation. The results demonstrate that the SVM model with an RBF kernel achieves superior performance with an accuracy of 97.30% and a False Negative Rate (FNR) of only 3.1% for high-severity threats. This research contributes a robust, data-driven approach to severity classification that effectively handles feature non-linearity and class imbalance, offering actionable insights for real-time security operations.
KeywordsMachine Learning; Model Classification; Cyber Security; Attack
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DOIhttps://doi.org/10.26555/ijain.v12i2.2245 |
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References
[1] A. Kumar and L. K. Singh, “A Study on Machine Learning-Based Models for Cyber Attack Classification and Severity Estimation,” Int. J. Comput. Appl., vol. 187, no. 41, pp. 65–70, Sep. 2025, doi: 10.5120/ijca2025925729.
[2] J. Note, E. Mullalli, and B. CICO, “Machine Learning Algorithms for Cyber Attack Detection And Classification,” in Proceedings of the International Conference on Computer Systems and Technologies 2024, New York, NY, USA: ACM, Jun. 2024, pp. 29–36. doi: 10.1145/3674912.3674937.
[3] N. Mohamed, “Artificial intelligence and machine learning in cybersecurity: a deep dive into state-of-the-art techniques and future paradigms,” Knowl. Inf. Syst., vol. 67, no. 8, pp. 6969–7055, Aug. 2025, doi: 10.1007/s10115-025-02429-y.
[4] M. K. Hasan, R. A. Abdulkadir, S. Islam, T. R. Gadekallu, and N. Safie, “A review on machine learning techniques for secured cyber-physical systems in smart grid networks,” Energy Reports, vol. 11, pp. 1268–1290, Jun. 2024, doi: 10.1016/j.egyr.2023.12.040.
[5] M. M. Alani, L. Mauri, and E. Damiani, “A two-stage cyber attack detection and classification system for smart grids,” Internet of Things, vol. 24, p. 100926, Dec. 2023, doi: 10.1016/j.iot.2023.100926.
[6] M. Alharby, “Evaluating machine learning approaches for multiple attack classification with improved computational efficiency in IoT networks,” Sci. Rep., vol. 15, no. 1, p. 39914, Nov. 2025, doi: 10.1038/s41598-025-23711-7.
[7] I. Avci and M. Koca, “Cybersecurity Attack Detection Model, Using Machine Learning Techniques,” Acta Polytech. Hungarica, vol. 20, no. 7, pp. 29–44, 2023, doi: 10.12700/APH.20.7.2023.7.2.
[8] S. T. Hamidou and A. Mehdi, “Enhancing IDS performance through a comparative analysis of Random Forest, XGBoost, and Deep Neural Networks,” Mach. Learn. with Appl., vol. 22, no. December, p. 100738, Dec. 2025, doi: 10.1016/j.mlwa.2025.100738.
[9] F. Ebrahimi, R. Javidan, R. Akbari, and Y. Hosseini, “Intrusion detection in the internet of things using convolutional neural networks: an explainable AI approach,” Cybersecurity, vol. 8, no. 1, p. 66, Sep. 2025, doi: 10.1186/s42400-025-00369-2.
[10] S. Naeem, Aqib Ali, Sania Anam, and Muhammad Munawar Ahmed, “Machine Learning for Intrusion Detection in Cyber Security: Applications, Challenges, and Recommendations,” Innov. Comput. Rev., vol. 2, no. 2, pp. 41–64, Dec. 2022, doi: 10.32350/icr.0202.03.
[11] H. M. R. U. Rehman et al., “A systematic literature study of machine learning techniques based intrusion detection: datasets, models, challenges, and future directions,” J. Big Data, vol. 12, no. 1, p. 264, Nov. 2025, doi: 10.1186/s40537-025-01323-2.
[12] K. Kioskli and N. Polemi, “Estimating Attackers’ Profiles Results in More Realistic Vulnerability Severity Scores,” in Human Factors in Cybersecurity, AHFE Open Access, 2022, pp. 138–150. doi: 10.54941/ahfe1002211.
[13] R. Cichocki and P. Wojcik, “Cybersecurity in Maritime Transport Systems: Threats, Trends, and Countermeasures in the Last Decade,” TransNav, Int. J. Mar. Navig. Saf. Sea Transp., vol. 19, no. 3, pp. 715–722, Sep. 2025, doi: 10.12716/1001.19.03.03.
[14] U. M. Alhaji, S. E. Adewumi, and V. I. Yemi-peters, “Classification of Phishing Attacks Using Machine Learning Algorithms: A Systematic Literature Review,” J. Adv. Math. Comput. Sci., vol. 40, no. 1, pp. 26–44, Jan. 2025, doi: 10.9734/jamcs/2025/v40i11960.
[15] D. Elreedy, A. F. Atiya, and F. Kamalov, “A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning,” Mach. Learn., vol. 113, no. 7, pp. 4903–4923, Jul. 2024, doi: 10.1007/s10994-022-06296-4.
[16] P. Zhao et al., “T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification,” in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, California: International Joint Conferences on Artificial Intelligence Organization, Jul. 2022, pp. 2406–2412. doi: 10.24963/ijcai.2022/334.
[17] S. Mateen, N. Nuthammachot, and K. Techato, “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan,” Open Geosci., vol. 16, no. 1, Feb. 2024, doi: 10.1515/geo-2022-0595.
[18] J. B. Jesudasan Peter et al., “SVM algorithm-based anomaly detection in network logs and firewall logs,” Indones. J. Electr. Eng. Comput. Sci., vol. 38, no. 3, p. 1642, Jun. 2025, doi: 10.11591/ijeecs.v38.i3.pp1642-1651.
[19] V. Sai Swaroop Reddy, “Cybersecurity Threat Prediction Using Machine Learning,” Int. J. Sci. Res., vol. 12, no. 4, pp. 1972–1976, Apr. 2023, doi: 10.21275/SR23048115831.
[20] s. Venkatraman, S. Kanthimathi, K. S. Jayasankar, T. Pranay Jiljith, and R. Jashwanth, “A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification,” IEEE Access, vol. 12, pp. 151515–151531, 2024, doi: 10.1109/ACCESS.2024.3478764.
[21] A. Z.K. Matloob, M. Ibrahim, and H. Kadem, “Machine Learning-Based Classification Models for Efficient DDoS Detection,” Int. J. Comput. Digit. Syst., vol. 17, no. 1, pp. 1–13, Jan. 2025, doi: 10.12785/ijcds/1571110617.
[22] N. R. Abid-Althaqafi and H. A. Alsalamah, “The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning,” Electronics, vol. 13, no. 1, p. 205, Jan. 2024, doi: 10.3390/electronics13010205.
[23] M. A. Tariq, “A Study on Comparative Analysis of Feature Selection Algorithms for Students Grades Prediction,” J. Inf. Organ. Sci., vol. 48, no. 1, pp. 133–147, Jun. 2024, doi: 10.31341/jios.48.1.7.
[24] S. Tarannum, M. S. Jalal, and M. N. Huda, “HALALCheck: A Multi-Faceted Approach for Intelligent Halal Packaged Food Recognition and Analysis,” IEEE Access, vol. 12, pp. 28462–28474, 2024, doi: 10.1109/ACCESS.2024.3367983.
[25] P. Mukherji, S. Rajput, and V. Mudaliar, “A novel accelerated sparse Support Vector Machine (AS-SVM) algorithm for binary classification of DNA sequences,” Franklin Open, vol. 13, no. December, p. 100427, Dec. 2025, doi: 10.1016/j.fraope.2025.100427.
[26] L. Bergamin and F. Aiolli, “An investigation into creating counterfactual examples for non-linear Support Vector Machines,” Neurocomputing, vol. 651, no. October, p. 130809, Oct. 2025, doi: 10.1016/j.neucom.2025.130809.
[27] A. Y. Shdefat, N. Mostafa, Z. Al-Arnaout, Y. Kotb, and S. Alabed, “Optimizing HAR Systems: Comparative Analysis of Enhanced SVM and k-NN Classifiers,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, p. 150, Jun. 2024, doi: 10.1007/s44196-024-00554-0.
[28] Y. Nataliani, C. Arthur, T. Wellem, K. D. Hartomo, and N. H. A. Wahab, “Multi-Objective k-Nearest Neighbor for Breast Cancer Detection,” JOIV Int. J. Informatics Vis., vol. 9, no. 1, p. 241, Jan. 2025, doi: 10.62527/joiv.9.1.2669.
[29] A. Mustafid, M. M. Pamuji, and S. Helmiyah, “A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification,” IJID (International J. Informatics Dev., vol. 9, no. 2, pp. 100–110, Dec. 2020, doi: 10.14421/ijid.2020.09207.
[30] M. Sabri, R. Verde, and A. Balzanella, “FWLMkNN: Efficient functional K-nearest neighbor based on clustering and functional data analysis,” Expert Syst. Appl., vol. 292, no. November, p. 128567, Nov. 2025, doi: 10.1016/j.eswa.2025.128567.
[31] K. C. Waghmare and B. A., “Modified K-nearest Neighbor Algorithm with Variant K Values,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 10, pp. 220–224, Oct. 2020, doi: 10.14569/IJACSA.2020.0111029.
[32] T. Winarti, H. Indriyawati, V. Vydia, and F. W. Christanto, “Performance comparison between naive bayes and k- nearest neighbor algorithm for the classification of Indonesian language articles,” IAES Int. J. Artif. Intell., vol. 10, no. 2, p. 452, Jun. 2021, doi: 10.11591/ijai.v10.i2.pp452-457.
[33] P. Changpetch, A. Pitpeng, S. Hiriote, and C. Yuangyai, “Integrating Data Mining Techniques for Naïve Bayes Classification: Applications to Medical Datasets,” Computation, vol. 9, no. 9, p. 99, Sep. 2021, doi: 10.3390/computation9090099.
[34] K. Zhang, J. Luo, C. Zhang, Y. Qiu, M. Shen, and H. Duan, “A remote sensing-based spatial prediction framework using a Naive Bayes approach for cyanobacterial blooms in eutrophic lakes of China,” J. Hydrol. Reg. Stud., vol. 62, no. December, p. 102894, Dec. 2025, doi: 10.1016/j.ejrh.2025.102894.
[35] N. Y. Nikitin and A. А. Stepashkin, “Classification of tensile test results of unidirectional carbon fiber-polysulfone composite material based on random forest, KNN and CNN methods,” Results Mater., vol. 28, no. December, p. 100788, Dec. 2025, doi: 10.1016/j.rinma.2025.100788.
[36] I. Afiqah et al., “Enhancing rock slope stability prediction using random forest machine learning: A case study,” China Geol., vol. 8, no. 4, pp. 691–706, Aug. 2025, doi: 10.31035/cg2023102.
[37] R. Blanquero, E. Carrizosa, P. Ramírez-Cobo, and M. R. Sillero-Denamiel, “Variable selection for Naïve Bayes classification,” Comput. Oper. Res., vol. 135, no. November, p. 105456, Nov. 2021, doi: 10.1016/j.cor.2021.105456.
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