Implementation of the Support Vector Machine (SVM) Algorithm to Improve the Accuracy of Computer Network Performance Predictions

Desi Irfan; Fahruzi Sirait; Rahadatul, Aisy Riadi; Aldi Indrawan; Juni Purwanto

doi:10.55227/ijhet.v4i1.271

Authors

Desi Irfan Information Technology, Faculty of Computer Science, Ika Bina Institute of Technology and Health 2) Information Systems, Faculty of Computer Science, Ika Bina Institute of Technology and Health 3) Software Engineering, Faculty of Computer Science, Dumai University
Fahruzi Sirait
Rahadatul, Aisy Riadi
Aldi Indrawan
Juni Purwanto Information Technology, Faculty of Computer Science, Ika Bina Institute of Technology and Health 2) Information Systems, Faculty of Computer Science, Ika Bina Institute of Technology and Health 3) Software Engineering, Faculty of Computer Science, Dumai University

DOI:

https://doi.org/10.55227/ijhet.v4i1.271

Keywords:

Support Vector Machine, Performance Prediction, Computer Networks, Machine Learning, Accuracy.

Abstract

Computer network performance is very important in supporting various digital activities, but systems often cannot accurately predict changes in performance, which can cause service disruptions and economic losses. This research aims to implement the Support Vector Machine (SVM) algorithm to increase the accuracy of network performance predictions based on parameters such as latency, packet loss, throughput and jitter. Data is collected through network simulation and real data monitoring, then processed with normalization and selection of relevant features. The SVM model is tested with various kernels, including linear, RBF, and polynomial, to find the best configuration. Performance evaluation uses accuracy, precision, recall, F1-score, and ROC-AUC metrics, with cross-validation to increase the reliability of the results. The results show that the RBF kernel provides a prediction accuracy of 92%, higher than baseline methods such as Decision Tree and Logistic Regression. This model shows its potential to be applied in computer network monitoring systems to predict network performance in real-time, with the possibility of wider implementation in artificial intelligence-based network applications. Therefore, this research not only contributes to machine learning theory in the field of computer networks, but also provides practical solutions that can improve the management and optimization of network performance in various environments that require fast and accurate data processing

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References

Anderson, R. (2020). Security engineering: A guide to building dependable distributed systems (3rd ed.). Wiley.

Bhardwaj, R., & Gupta, S. (2019). Comparative study of phishing detection algorithms. International Journal of Computer Science and Information Security, 17(8), 35-40.

Desai, D., & Patel, S. (2021). Detection of phishing attacks through web traffic analysis and machine learning techniques. Journal of Digital Forensics, Security, and Law.

Gupta, S., & Gupta, A. (2019). Social engineering in phishing: A comprehensive review. International Journal of Information Security, 18(2), 127-138.

Kaufman, C., Perlman, R., & Speciner, M. (2015). Network security: Private communication in a public world (2nd ed.). Prentice Hall.

Kaur, H., & Bedi, P. (2019). Machine learning algorithms in detecting phishing emails. Journal of Cyber Security and Privacy, 5(2), 120-135.

Kumar, N., & Choudhary, R. (2020). Detecting phishing attacks using heuristic and machine learning methods. Cybersecurity and Privacy, 1(3), 45-56.

Mitnick, K. D., & Simon, W. L. (2020). Social engineering: The art of human hacking. Journal of Information Security, 14(4), 250-267.

Padhy, S., & Soni, R. (2020). Phishing attacks: A review of techniques and detection methods. International Journal of Computer Applications, 175(3), 5-12.

Peltier, T. R. (2016). Information security risk analysis (2nd ed.). CRC Press.

Sharma, P., & Bhagat, S. (2020). Phishing detection using hybrid model of decision trees and Naive Bayes. International Journal of Information Technology and Computer Science, 12(1), 11-18.

Singh, A., & Arora, A. (2021). Machine learning algorithms for phishing detection: A systematic review. Journal of Computer Networks and Communications, 2021, 1-12.

Stallings, W. (2017). Network security essentials: Applications and standards (6th ed.). Pearson Education.

Stojanovic, J., & Kostic, D. (2019). Phishing attack detection: A review of machine learning approaches. Computers & Security, 85, 87-107.

Tittel, E. (2016). Hacking exposed: Network security secrets & solutions (7th ed.). McGraw-Hill Education.

Whitman, M. E., & Mattord, H. J. (2018). Principles of information security (6th ed.). Cengage Learning.

Zhang, X., & Yang, Y. (2021). Phishing email detection using machine learning: A case study. Journal of Computer Science and Technology, 36(1), 52-63.

Zhao, J., & Li, X. (2020). An overview of machine learning techniques for cybersecurity applications. Journal of Cybersecurity and Privacy, 6(4), 180-193.