1
Digital Charging Solutions GmbH, Germany
(Mustafa.iessa@gmail.com)
2
University of Technology and Applied Science, Shinas, Oman
(aaras.kraidi@shct.edu.om)
Abstract :
The use of machine learning methods in cybersecurity is only one of many examples of how this once-emerging innovation has entered the mainstream. Anomaly-based identification of common assaults on vital infrastructures is only one instance of the various applications of malware analysis. Scholars are using machine learning-based identification in numerous cybersecurity solutions since signature-based approaches are inadequate at identifying zero-day threats or even modest modifications of established assaults. In this work, we introduce the machine-learning models-based security framework to detect cyber-attacks. This paper used three machine learning models Logistic Regression, Random Forest, and K-Nearest Neighbor This framework not only reduces the computational difficulty of the framework by minimizing the feature parameters, but it also performs well in terms of accuracy in forecasting unknown scenarios in the tests. Finally, we ran trials using cybersecurity datasets to measure the machine learning model's performance using metrics including precision, recall, and accuracy.
Keywords :
Machine Learning; Cybersecurity; Cyberattacks; Logistic Regression; K-Nearest Neighbor; Random Forest
References :
[1] A. Handa, A. Sharma, and S. K. Shukla, “Machine learning in cybersecurity: A review,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 9, no. 4, p. e1306, 2019.
[2] S. Dua and X. Du, Data mining and machine learning in cybersecurity. CRC press, 2016.
[3] J. B. Fraley and J. Cannady, “The promise of machine learning in cybersecurity,” in SoutheastCon 2017, IEEE, 2017, pp. 1–6.
[4] I. H. Sarker, A. S. M. Kayes, S. Badsha, H. Alqahtani, P. Watters, and A. Ng, “Cybersecurity data science: an overview from machine learning perspective,” J. Big data, vol. 7, pp. 1–29, 2020.
[5] P. Dasgupta and J. Collins, “A survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks,” AI Mag., vol. 40, no. 2, pp. 31–43, 2019.
[6] Y. Miao, C. Chen, L. Pan, Q.-L. Han, J. Zhang, and Y. Xiang, “Machine learning–based cyber attacks targeting on controlled information: A survey,” ACM Comput. Surv., vol. 54, no. 7, pp. 1–36, 2021.
[7] I. H. Sarker, Y. B. Abushark, F. Alsolami, and A. I. Khan, “Intrudtree: a machine learning based cyber security intrusion detection model,” Symmetry (Basel)., vol. 12, no. 5, p. 754, 2020.
[8] V. Ford and A. Siraj, “Applications of machine learning in cyber security,” in Proceedings of the 27th international conference on computer applications in industry and engineering, IEEE Xplore Kota Kinabalu, Malaysia, 2014.
[9] R. Prasad, V. Rohokale, R. Prasad, and V. Rohokale, “Artificial intelligence and machine learning in cyber security,” Cyber Secur. lifeline Inf. Commun. Technol., pp. 231–247, 2020.
[10] G. Apruzzese, M. Colajanni, L. Ferretti, A. Guido, and M. Marchetti, “On the effectiveness of machine and deep learning for cyber security,” in 2018 10th international conference on cyber Conflict (CyCon), IEEE, 2018, pp. 371–390.
[11] K. Shaukat et al., “Performance comparison and current challenges of using machine learning techniques in cybersecurity,” Energies, vol. 13, no. 10, p. 2509, 2020.
[12] R. Das and T. H. Morris, “Machine learning and cyber security,” in 2017 international conference on computer, electrical & communication engineering (ICCECE), IEEE, 2017, pp. 1–7.
[13] S. A. Salloum, M. Alshurideh, A. Elnagar, and K. Shaalan, “Machine learning and deep learning techniques for cybersecurity: a review,” in Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020), Springer, 2020, pp. 50–57.
[14] P. Sornsuwit and S. Jaiyen, “A new hybrid machine learning for cybersecurity threat detection based on adaptive boosting,” Appl. Artif. Intell., vol. 33, no. 5, pp. 462–482, 2019.
[15] I. F. Kilincer, F. Ertam, and A. Sengur, “Machine learning methods for cyber security intrusion detection: Datasets and comparative study,” Comput. Networks, vol. 188, p. 107840, 2021.
[16] M. A. Teixeira, T. Salman, M. Zolanvari, R. Jain, N. Meskin, and M. Samaka, “SCADA system testbed for cybersecurity research using machine learning approach,” Futur. Internet, vol. 10, no. 8, p. 76, 2018.
[17] S. Strecker, W. Van Haaften, and R. Dave, “An analysis of IoT cyber security driven by machine learning,” in Proceedings of International Conference on Communication and Computational Technologies: ICCCT 2021, Springer, 2021, pp. 725–753.
[18] R. A. Calix, S. B. Singh, T. Chen, D. Zhang, and M. Tu, “Cyber security tool kit (CyberSecTK): A Python library for machine learning and cyber security,” Information, vol. 11, no. 2, p. 100, 2020.
[19] J. L. Speiser, M. E. Miller, J. Tooze, and E. Ip, “A comparison of random forest variable selection methods for classification prediction modeling,” Expert Syst. Appl., vol. 134, pp. 93–101, 2019.
[20] T. Hengl, M. Nussbaum, M. N. Wright, G. B. M. Heuvelink, and B. Gräler, “Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables,” PeerJ, vol. 6, p. e5518, 2018.
[21] E. Christodoulou, J. Ma, G. S. Collins, E. W. Steyerberg, J. Y. Verbakel, and B. Van Calster, “A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models,” J. Clin. Epidemiol., vol. 110, pp. 12–22, 2019.
[22] D. Tien Bui, T. A. Tuan, H. Klempe, B. Pradhan, and I. Revhaug, “Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree,” Landslides, vol. 13, pp. 361–378, 2016.
[23] R. Goyal, P. Chandra, and Y. Singh, “Suitability of KNN regression in the development of interaction based software fault prediction models,” Ieri Procedia, vol. 6, pp. 15–21, 2014.
[24] S. B. Imandoust and M. Bolandraftar, “Application of k-nearest neighbor (knn) approach for predicting economic events: Theoretical background,” Int. J. Eng. Res. Appl., vol. 3, no. 5, pp. 605–610, 2013.
| Style | # |
|---|---|
| MLA | Mustafa El-Taie , Aaras Y.Kraidi. "Cybersecurity Detection Model using Machine Learning Techniques." Journal of Cybersecurity and Information Management, Vol. 12, No. 1, 2023 ,PP. 41-49 (Doi : https://doi.org/10.54216/JCIM.120104) |
| APA | Mustafa El-Taie , Aaras Y.Kraidi. (2023). Cybersecurity Detection Model using Machine Learning Techniques. Journal of Journal of Cybersecurity and Information Management, 12 ( 1 ), 41-49 (Doi : https://doi.org/10.54216/JCIM.120104) |
| Chicago | Mustafa El-Taie , Aaras Y.Kraidi. "Cybersecurity Detection Model using Machine Learning Techniques." Journal of Journal of Cybersecurity and Information Management, 12 no. 1 (2023): 41-49 (Doi : https://doi.org/10.54216/JCIM.120104) |
| Harvard | Mustafa El-Taie , Aaras Y.Kraidi. (2023). Cybersecurity Detection Model using Machine Learning Techniques. Journal of Journal of Cybersecurity and Information Management, 12 ( 1 ), 41-49 (Doi : https://doi.org/10.54216/JCIM.120104) |
| Vancouver | Mustafa El-Taie , Aaras Y.Kraidi. Cybersecurity Detection Model using Machine Learning Techniques. Journal of Journal of Cybersecurity and Information Management, (2023); 12 ( 1 ): 41-49 (Doi : https://doi.org/10.54216/JCIM.120104) |
| IEEE | Mustafa El-Taie, Aaras Y.Kraidi, Cybersecurity Detection Model using Machine Learning Techniques, Journal of Journal of Cybersecurity and Information Management, Vol. 12 , No. 1 , (2023) : 41-49 (Doi : https://doi.org/10.54216/JCIM.120104) |