Real-time Intrusion Detection System Using Machine Learning for Enhanced and Efficient Cybersecurity in Organizations
Article Sidebar
Main Article Content
Abstract
The purpose of this research is to create a real-time cyber intrusion detection system using machine learning to strengthen and enhance cybersecurity within organizations. RapidMiner Studio was used to analyze intrusion data with 4 algorithms: Decision Tree, Naïve Bayes, Random Forest, and Gradient Boosted Trees. The algorithms were evaluated and compared using the Royal Air Force Cyber Intrusion Detection Dataset (RTAF Dataset) to determine their accuracy based on precision and recall values. The top 2 from 4 performing algorithms were then combined using ensemble machine learning techniques. The resulting algorithm was evaluated and compared based on precision and recall values. The algorithm with the highest performance was then used to create a real-time cyber intrusion detection system using machine learning. The results showed that the real-time cyber intrusion detection system using machine learning for strengthening and enhancing cybersecurity in organizations, developed using a software development process combined with ensemble machine learning-based real-time cyber intrusion detection using the Gradient Boosted Trees algorithm in conjunction with the Naïve Bayes algorithm using the Stacking technique, can provide prediction results with an accuracy of 99.77% and a precision of 88.59%.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Article Accepting Policy
The editorial board of Thai-Nichi Institute of Technology is pleased to receive articles from lecturers and experts in the fields of business administration, languages, engineering and technology written in Thai or English. The academic work submitted for publication must not be published in any other publication before and must not be under consideration of other journal submissions. Therefore, those interested in participating in the dissemination of work and knowledge can submit their article to the editorial board for further submission to the screening committee to consider publishing in the journal. The articles that can be published include solely research articles. Interested persons can prepare their articles by reviewing recommendations for article authors.
Copyright infringement is solely the responsibility of the author(s) of the article. Articles that have been published must be screened and reviewed for quality from qualified experts approved by the editorial board.
The text that appears within each article published in this research journal is a personal opinion of each author, nothing related to Thai-Nichi Institute of Technology, and other faculty members in the institution in any way. Responsibilities and accuracy for the content of each article are owned by each author. If there is any mistake, each author will be responsible for his/her own article(s).
The editorial board reserves the right not to bring any content, views or comments of articles in the Journal of Thai-Nichi Institute of Technology to publish before receiving permission from the authorized author(s) in writing. The published work is the copyright of the Journal of Thai-Nichi Institute of Technology.
References
National Cyber Security Agency, “Cyber Threat Statistics,” 2023. [Online]. Available https://ncsa.or.th/service-statistics.html
Royal Thai Air Force, “Royal Thai air force strategy for 20 years (B.E. 2561–2580) (revised version),” 2020. [Online]. Available: https://welcome-page.rtaf.mi.th/blog/e-ksaarephyaephr-11/yuththsaastrk-ngthaph-aakaas-20-pii-ph-s-2561-2580-38
A. H. Janabi, T. Kanakis, and M. Johnson, “Overhead reduction technique for software-defined network based intrusion detection systems,” IEEE Access, vol. 10, pp. 66481–66491, 2022.
A. S. A. Aziz, S. E.-O. Hanafi, and A. E. Hassanien, “Comparison of classification techniques applied for network intrusion detection and classification,” J. Appl. Logic, vol. 24, pp. 109–118, Nov. 2017.
G. Karatas, O. Demir, and O. K. Sahingoz, “Increasing the performance of machine learning-based IDSs on an imbalanced and up-to-date dataset,” IEEE Access, vol. 8, pp. 32150–32162, 2020.
A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, “Survey of intrusion detection systems: techniques, datasets and challenges,” Cybersecurity, vol. 2, no. 1, 2019, Art. no. 20, doi: 10.1186/s42400-019-0038-7.
T. A. Alamiedy, M. Anbar, A. K. Al-Ani, B. N. Al-Tamimi, and N. Faleh, “Review on feature selection algorithms for anomaly-based intrusion detection system,” in Proc. 3rd Int. Conf. Reliable Inf. and Commun. Technol., Kuala Lumpur, Malaysia, Jun. 2018, pp. 605–619.
I. Garcia-Magarino, G. Gray, R. Lacuesta, and J. Lloret, “Survivability strategies for emerging wireless networks with data mining techniques: A case study with NetLogo and RapidMiner,” IEEE Access, vol. 6, pp. 27958–27970, 2018.
A. Karim, M. Shahroz, K. Mustofa, S. B. Belhaouari, and S. R. K. Joga, “Phishing detection system through hybrid machine learning based on URL,” IEEE Access, vol. 11, pp. 36805–36822, 2023.
T. M. Mitchell, Machine Learning. New York, NY, USA: McGraw-Hill, 1997.
H. Yin, M. Xue, Y. Xiao, K. Xia, and G. Yu, “Intrusion detection classification model on an improved k-dependence Bayesian network,” IEEE Access, vol. 7, pp. 157555–157563, 2019.
L. Jian and K.-T. Chau, “Analytical calculation of magnetic field distribution in coaxial magnetic gears,” Prog. Electromagn. Res., vol. 92, pp. 1–16, 2009.
N. Mahdi Abdulkareem and A. M. Abdulazeez, “Machine learning classification based on random forest algorithm: A review,” Int. J. Sci. Bus., vol. 5, no. 2, pp. 128–142, 2021.
W. Li, W. Wang, and W. Huo, “RegBoost: A gradient boosted multivariate regression algorithm,” Int. J. Crowd Sci., vol. 4, no. 1, pp. 60–72, 2020.
D. Stiawan, “An approach for optimizing ensemble intrusion detection systems,” IEEE Access, vol. 9, pp. 6930–6947, 2021.
H. Luo, F. Cheng, H. Yu, and Y. Yi, “SDTR: Soft decision tree regressor for tabular data,” IEEE Access, vol. 9, pp. 55999–56011, 2021.
M. Mohy-Eddine, A. Guezzaz, S. Benkirane, M. Azrour, and Y. Farhaoui, “An ensemble learning based intrusion detection model for industrial IoT security,” Big Data Mining and Analytics, vol. 6, no. 3, pp. 273–287, 2023.
S. Ismail, Z. El Mrabet, and H. Reza, “An ensemble-based machine learning approach for cyber-attacks detection in wireless sensor networks,” Appl. Sci., vol. 13, no. 1, 2022, Art. no. 30.
A. Agarwal, A. Agarwal, D. K. Verma, D. Tiwari, and R. Pandey, “A review on software development life cycle,” Int. J. Scientific Res. Comput. Sci., Eng. Inf. Technol., vol. 9, no. 3, pp. 384–388, 2023, doi: 10.32628/cseit2390387.
C. Qi, J. Diao, and L. Qiu, “On estimating model in feature selection with cross-validation,” IEEE Access, vol. 7, pp. 33454–33463, 2019.
G.-P. Fernando, A.-A. H. Brayan, A. M. Florina, C.-B. Liliana, A.-M. Héctor-Gabriel, and T.-S. Reinel, “Enhancing intrusion detection in IoT communications through ML model generalization with a new dataset (IDSAI),” IEEE Access, vol. 11, pp. 70542–70559, 2023.
L. Zou, X. Luo, Y. Zhang, X. Yang, and X. Wang, “HC-DTTSVM: A network intrusion detection method based on decision tree twin support vector machine and hierarchical clustering,” IEEE Access, vol. 11, pp. 21404–21416, 2023.
S. Ameer, “Comparative analysis of machine learning techniques for predicting air quality in smart cities,” IEEE Access, vol. 7, pp. 128325–128338, 2019.
W. Alhakami, A. ALharbi, S. Bourouis, R. Alroobaea, and N. Bouguila, “Network anomaly intrusion detection using a nonparametric Bayesian approach and feature selection,” IEEE Access, vol. 7, pp. 52181–52190, 2019.
X. Gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, “An adaptive ensemble machine learning model for intrusion detection,” IEEE Access, vol. 7, pp. 82512–82521, 2019.
Y. Yu and N. Bian, “An intrusion detection method using few-shot learning,” IEEE Access, vol. 8, pp. 49730–49740, 2020.
Z. Wu, H. Zhang, P. Wang, and Z. Sun, “RTIDS: A robust transformer-based approach for intrusion detection system,” IEEE Access, vol. 10, pp. 64375–64387, 2022.
S. Gibson, B. Issac, L. Zhang, and S. M. Jacob, “Detecting spam email with machine learning optimized with bio-inspired metaheuristic algorithms,” IEEE Access, vol. 8, pp. 187914–187932, 2020.
R. Zhao, Y. Mu, L. Zou, and X. Wen, “A hybrid intrusion detection system based on feature selection and weighted stacking classifier,” IEEE Access, vol. 10, pp. 71414–71426, 2022.
A. A. Taha and S. J. Malebary, “An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine,” IEEE Access, vol. 8, pp. 25579–25587, 2020.