★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★Open research. Global perspectives.
editor@theamericanjournals.comPublisher Home ↗
Engineering and Technology OPEN ACCESS

COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CYBERSECURITY ATTACK SUCCESS: A PERFORMANCE EVALUATION

Md Abu Sayed
Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA
Badruddowza
Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
Sonjoy Kumar Dey
McComish Department of Electrical Engineering and Computer Science, South Dakota State University, USA
Md Shohail Uddin Sarker
Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
Abdullah Al Mamun
Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
Norun Nabi
Master of Science in Information Technology (MSIT)- Washington University of Science and Technology (WUST), Alexandria, Virginia, USA
Fuad Mahmud
Department of Information Assurance and Cybersecurity, Gannon University, USA
Md Khorshed Alam
Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA
Md Tarek Hasan
Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA
Md Rashed Buiya
Department of Computer Science, California State University, Dominguez Hills, USA
Mashaeikh Zaman Md. Eftakhar Choudhury
Master of Social Science in Security Studies, Bangladesh University of Professional (BUP), Dhaka, Bangladesh
DOI Crossref DOI 10.37547/tajet/Volume06Issue09-10
tajet 2024
VOL. 6 / NO. 09 SEPTEMBER
FULL TEXT PP. 81-91
DOWNLOAD PDF

View all articles in this issue ↗

VOLUME 6
ISSUE 09
YEAR 2024
PAGES 81-91
RESEARCH OVERVIEW

Abstract

This study explores the effectiveness of various machine learning algorithms in predicting the success of cybersecurity attacks by analyzing historical attack data. We evaluated five prominent algorithms—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and K-Nearest Neighbors (KNN)—based on their performance metrics, including accuracy, precision, recall, F1-Score, and AUC-ROC. Our results indicate that Random Forest outperforms the other algorithms, achieving the highest accuracy (90%), precision (88%), recall (85%), F1-Score (86%), and AUC-ROC (0.92). Gradient Boosting also demonstrated strong performance with an accuracy of 88% and an AUC-ROC of 0.90, though it required more computational resources. Logistic Regression and SVM provided moderate results, while K-Nearest Neighbors showed the least effectiveness due to its lower performance metrics. The comparative analysis highlights Random Forest as the most effective model for predicting cybersecurity attack success, offering superior performance in handling complex data and distinguishing between attack outcomes. These findings provide valuable insights for improving cybersecurity strategies and selecting appropriate machine-learning models for threat prediction.

RESEARCH TOPICS

Keywords

Machine Learning Cybersecurity Random Forest

Downloads

Download data is not yet available.
SOURCES & SCHOLARSHIP

References

  1. Cover, T. M., & Hart, P. E. (1967). Nearest-neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964
  2. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
  3. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451
  4. Li, Y., Xu, Y., & Zhang, Q. (2020). Machine learning for cybersecurity: A survey. Journal of Computer Science and Technology, 35(1), 8-20. https://doi.org/10.1007/s11390-019-1917-8
  5. Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18-22. https://CRAN.R-project.org/doc/Rnews/
  6. Menard, S. (2021). Applied logistic regression analysis (3rd ed.). Sage Publications.
  7. NIST. (2021). NIST cybersecurity framework. National Institute of Standards and Technology. https://www.nist.gov/cyberframework
  8. Sommer, R., & Paxson, V. (2019). The role of machine learning in cybersecurity. ACMComputing Surveys, 52*(3), 1-36. https://doi.org/10.1145/3287320
  9. Chen, T., Song, L., & He, X. (2021). Gradient boosting for cybersecurity. Journal of Cybersecurity, 7(4), 122-134. https://doi.org/10.1016/j.jocs.2021.100056
  10. Gao, Y., Han, X., & Zhang, Q. (2018). Application of logistic regression in cybersecurity. IEEE Access, 6, 7891-7900. https://doi.org/10.1109/ACCESS.2018.2883235
  11. Kang, M., Kim, Y., & Lee, S. (2019). Random Forest algorithm for threat prediction. Computers & Security, 86, 261-275. https://doi.org/10.1016/j.cose.2019.05.002
  12. Liu, X., Wang, L., & Zhang, Y. (2020). Support vector machine-based detection of network attacks. Information Sciences, 512, 560-572. https://doi.org/10.1016/j.ins.2019.10.032
  13. Zhou, Z., Yu, Z., & Zhang, J. (2017). K-Nearest Neighbors in network security applications. Future Generation Computer Systems, 75, 68-80. https://doi.org/10.1016/j.future.2017.02.010
  14. Mozumder, M. A. S., Sweet, M. M. R., Nabi, N., Tusher, M. I., Modak, C., Hasan, M., ... & Prabha, M. (2024). Revolutionizing Organizational Decision-Making for Banking Sector: A Machine Learning Approach with CNNs in Business Intelligence and Management. Journal of Business and Management Studies, 6(3), 111-118.
  15. Bhuiyan, M. S., Chowdhury, I. K., Haider, M., Jisan, A. H., Jewel, R. M., Shahid, R., ... & Siddiqua, C. U. (2024). Advancements in early detection of lung cancer in public health: a comprehensive study utilizing machine learning algorithms and predictive models. Journal of Computer Science and Technology Studies, 6(1), 113-121.
  16. Nabi, N., Tusher, M. I., Modak, C., Hasan, M., ... & Prabha, M. (2024). Revolutionizing Organizational Decision-Making for Banking Sector: A Machine Learning Approach with CNNs in Business Intelligence and Management. Journal of Business and Management Studies, 6(3), 111-118.
  17. Rahman, M. A., Modak, C., Mozumder, M. A. S., Miah, M. N. I., Hasan, M., Sweet, M. M. R., ... & Alam, M. (2024). Advancements in Retail Price Optimization: Leveraging Machine Learning Models for Profitability and Competitiveness. Journal of Business and Management Studies, 6(3), 103-110.
  18. Shahid, R., Mozumder, M. A. S., Sweet, M. M. R., Hasan, M., Alam, M., Rahman, M. A., ... & Islam, M. R. (2024). Predicting Customer Loyalty in the Airline Industry: A Machine Learning Approach Integrating Sentiment Analysis and User Experience. International Journal on Computational Engineering, 1(2), 50-54.
  19. Modak, C., Ghosh, S. K., Sarkar, M. A. I., Sharif, M. K., Arif, M., Bhuiyan, M., ... & Devi, S. (2024). Machine Learning Model in Digital Marketing Strategies for Customer Behavior: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making. Journal of Economics, Finance and Accounting Studies, 6(3), 178-186.
  20. Mozumder, M. A. S., Nguyen, T. N., Devi, S., Arif, M., Ahmed, M. P., Ahmed, E., ... & Uddin, A. (2024). Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry. Journal of Computer Science and Technology Studies, 6(3), 35-41.
  21. Arif, M., Hasan, M., Al Shiam, S. A., Ahmed, M. P., Tusher, M. I., Hossan, M. Z., ... & Imam, T. (2024). Predicting Customer Sentiment in Social Media Interactions: Analyzing Amazon Help Twitter Conversations Using Machine Learning. International Journal of Advanced Science Computing and Engineering, 6(2), 52-56.
  22. Md Al-Imran, Salma Akter, Md Abu Sufian Mozumder, Rowsan Jahan Bhuiyan, Md Al Rafi, Md Shahriar Mahmud Bhuiyan, Gourab Nicholas Rodrigues, Md Nazmul Hossain Mir, Md Amit Hasan, Ashim Chandra Das, & Md. Emran Hossen. (2024). EVALUATING MACHINE LEARNING ALGORITHMS FOR BREAST CANCER DETECTION: A STUDY ON ACCURACY AND PREDICTIVE PERFORMANCE. The American Journal of Engineering and Technology, 6(09), 22–33. https://doi.org/10.37547/tajet/Volume06Issue09-04
  23. Md Abu Sufian Mozumder, Fuad Mahmud, Md Shujan Shak, Nasrin Sultana, Gourab Nicholas Rodrigues, Md Al Rafi, Md Zahidur Rahman Farazi, Md Razaul Karim, Md. Sayham Khan, & Md Shahriar Mahmud Bhuiyan. (2024). Optimizing Customer Segmentation in the Banking Sector: A Comparative Analysis of Machine Learning Algorithms. Journal of Computer Science and Technology Studies, 6(4), 01–07. https://doi.org/10.32996/jcsts.2024.6.4.1
DISCOVER RELATED RESEARCH

Similar Articles

Explore published articles on related topics and research interests.

1 2 > >> 
You may also start an advanced similarity search for this article.
CONTINUE EXPLORING

Most read articles by the same author(s)

Explore other published research by the authors of this article.

1 2 > >>