CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Revolutionizing Network Security with AI and Machine Learning Solutions

by Tahir Bashir, Najeeb Abbas Al-Sammarraie
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 53
Year of Publication: 2024
Authors: Tahir Bashir, Najeeb Abbas Al-Sammarraie
10.5120/ijca2024924217

Tahir Bashir, Najeeb Abbas Al-Sammarraie . Revolutionizing Network Security with AI and Machine Learning Solutions. International Journal of Computer Applications. 186, 53 ( Dec 2024), 35-42. DOI=10.5120/ijca2024924217

@article{ 10.5120/ijca2024924217,
author = { Tahir Bashir, Najeeb Abbas Al-Sammarraie },
title = { Revolutionizing Network Security with AI and Machine Learning Solutions },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2024 },
volume = { 186 },
number = { 53 },
month = { Dec },
year = { 2024 },
issn = { 0975-8887 },
pages = { 35-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number53/revolutionizing-network-security-with-ai-and-machine-learning-solutions/ },
doi = { 10.5120/ijca2024924217 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-12-07T02:20:23.464426+05:30
%A Tahir Bashir
%A Najeeb Abbas Al-Sammarraie
%T Revolutionizing Network Security with AI and Machine Learning Solutions
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 53
%P 35-42
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As cyber threats evolve and grow more sophisticated, traditional network security approaches are struggling to keep pace with the increasing complexity of modern attacks. This paper investigates how Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing network security by automating critical processes such as threat detection and response. Traditional security models rely heavily on manual monitoring and predefined rules, which can result in delayed responses and missed threats. AI and ML technologies offer an alternative by enabling real-time analysis of network traffic, the identification of anomalies, and proactive threat mitigation. These systems are capable of learning from historical data, improving their detection capabilities over time, and adapting to new and unknown threats, including zero-day vulnerabilities. This research is based on a comprehensive review of existing literature and case studies from industries where AI and ML have been successfully integrated into security frameworks. The findings illustrate the effectiveness of AI and ML in improving security performance, reducing human error, and enhancing operational efficiency. Organizations that have adopted these technologies report faster response times, more accurate threat detection, and fewer false positives. However, the adoption of AI and ML also presents challenges, including the need for substantial initial investments, technical integration with legacy systems, and the requirement for skilled personnel to manage and optimize these technologies. Despite these challenges, AI and ML are becoming indispensable tools for organizations seeking to bolster their cybersecurity capabilities. As cyberattacks grow increasingly complex, the ability to automate critical security tasks and respond to threats in real time positions AI and ML as essential components of modern network defense strategies. This research underscores their potential to transform network security and highlights their role in protecting against the ever-increasing threat of cyberattacks.

References
  1. Ahmad, I., Yusoff, M., & Musa, S. (2021). A survey on artificial intelligence techniques in cybersecurity. Journal of Information Security and Applications, 58, 102719. https://doi.org/10.1016/j.jisa.2021.102719
  2. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cybersecurity intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176. https://doi.org/10.1109/COMST.2015.2494502
  3. Dong, Z., Chen, H., & Zhou, Y. (2022). AI-driven network security: Threat detection and prevention using machine learning algorithms. Journal of Cybersecurity, 18(3), 241-259.
  4. Garcia-Teodoro, P., Diaz-Verdejo, J., Macia-Fernandez, G., & Vazquez, E. (2009). Anomaly-based network intrusion detection: Techniques, systems, and challenges. Computers & Security, 28(1-2), 18-28. https://doi.org/10.1016/j.cose.2008.08.003
  5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
  6. Hinton, G., Krizhevsky, A., & Wang, S. D. (2017). ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
  7. Sarker, I. H., Kayes, A. S. M., & Watters, P. (2020). Cybersecurity data science: An overview from machine learning perspective. Journal of Big Data, 7(1), 1-29. https://doi.org/10.1186/s40537-020-00318-5
  8. Shamshirband, S., Anuar, N. B., & Kiah, M. L. M. (2014). A review of intrusion detection systems in cloud computing. Journal of Network and Computer Applications, 36(1), 42-57. https://doi.org/10.1016/j.jnca.2013.06.016
  9. Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (pp. 305-316). IEEE. https://doi.org/10.1109/SP.2010.25
  10. Wang, T., Chen, J., & Yu, X. (2022). AI-based real-time anomaly detection for cybersecurity in enterprise networks. Journal of Network and Systems Management, 30(2), 1-19.
  11. Zhang, Y., & Paxson, V. (2021). Exploring machine learning for automated network anomaly detection: A systematic review. IEEE Transactions on Network and Service Management, 18(2), 1853-1865. https://doi.org/10.1109/TNSM.2021.3061537
  12. Anderson, H. R., & McGrew, D. (2017). Machine learning for encrypted malware traffic classification: Accounting for noisy labels and non-stationarity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1723-1732).https://doi.org/10.1145/3097983.3098163
  13. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Esesn, B. C., Awwal, A. A. S., & Asari, V. K. (2019). A state-of-the-art survey on deep learning theory and architecture. Electronics, 8(3), 292.
  14. Vinayakumar, R., Soman, K. P., & Poornachandran, P. (2017). Applying deep learning approaches for network traffic prediction and classification. Procedia Computer Science, 132, 298-305.
  15. Li, Y., & Guo, L. (2019). Machine learning applications in cybersecurity: A review. Journal of Information Security, 10(3), 147-159.
  16. Chen, X., Liu, Y., & Huang, H. (2021). An efficient deep learning model for network intrusion detection using packet-based data. IEEE Access, 9, 174025-174036. https://doi.org/10.1109/ACCESS.2021.3074974
  17. Feng, X., Zhou, C., & Xu, L. (2021). Real-time anomaly detection based on incremental machine learning for internet of things. Computers & Security, 96, 101924. https://doi.org/10.1016/j.cose.2020.101924
  18. Javidi, M., Soleymani, M., & Wang, C. (2022). AI-driven deep learning methods for advanced network security. Computers & Electrical Engineering, 101, 108038.
  19. Brownlee, J. (2018). Machine learning mastery with Python. Machine Learning Mastery.
  20. Lippmann, R. P., & Cunningham, R. K. (1999). Improving intrusion detection performance using keyword selection and neural networks. Proceedings of the DARPA Information Survivability Conference and Exposition (Vol. 2, pp. 302-313). https://doi.org/10.1109/DISCEX.1999.816620
  21. Berman, D. S., Buczak, A. L., Chavis, J. S., & Corbett, C. L. (2019). A survey of deep learning methods for cybersecurity. Information, 10(4), 122.
  22. Bezzazi, H., Alqatawna, J., & Mezrag, M. (2020). Predicting zero-day attacks using machine learning. Procedia Computer Science, 170, 78-85. https://doi.org/10.1016/j.procs.2020.03.010
  23. Ahmed, M., & Mahmood, A. N. (2016). A review of anomaly detection techniques in telecommunications networks. Journal of Network and Computer Applications, 40(2), 138-155. https://doi.org/10.1016/j.jnca.2014.12.015
  24. Zhou, X., & Leckie, C. (2020). A survey on machine learning methods for cybersecurity applications. Artificial Intelligence Review, 43(3), 321-340.
  25. Kim, G., Lee, S., & Kim, S. (2021). Deep learning-based anomaly detection in cybersecurity. Journal of Systems Architecture, 119, 102-109.
  26. Al-Tameemi, H. (2021). Machine learning applications in network security: A review of research trends and open challenges. Future Generation Computer Systems, 125, 406-420.
  27. Rani, A., & Agarwal, R. (2021). Machine learning models for predictive network security in enterprise environments. IEEE Access, 9, 172825-172837. https://doi.org/10.1109/ACCESS.2021.3127245
  28. Lin, P., Luo, X., & Chen, Y. (2019). A machine learning approach for detecting anomalies in cybersecurity data. Journal of Information Security, 10(2), 136-148.
  29. Aggarwal, C. C. (2017). Outlier analysis. Springer.
  30. Zhang, K., & Shi, W. (2022). Leveraging AI in network security: Anomaly detection through machine learning techniques. IEEE Access, 10, 45117-45128. https://doi.org/10.1109/ACCESS.2022.3159823
  31. Azad, S., Bhunia, S. S., & Debnath, N. C. (2022). Artificial intelligence-based network security solutions for IoT. Sensors, 22(1), 193.
  32. Mohammadi, H., & Jafari, S. (2022). Comparative analysis of machine learning algorithms for network security intrusion detection systems. Computers & Security, 105, 102239. https://doi.org/10.1016/j.cose.2020.102239
  33. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
  34. Kieu, T. N., Ha, Q. D., & Thang, D. (2021). AI-driven solutions for improving the detection of network intrusions. Journal of Information Security, 12(4), 223-236.
  35. Shin, J., & Kim, T. H. (2021). Machine learning approaches to secure cloud-based networks. Journal of Cloud Computing, 10(2), 12.
  36. Liang, C., Li, J., & Sun, W. (2020). Advanced threat detection in industrial networks using machine learning. *Journal of Network and Computer
Index Terms

Computer Science
Information Sciences

Keywords

Artificial intelligence Machine Learning Network Security Cybersecurity Zero Trust Architecture Threat Detection Automation Cloud Security Enterprise Networks

Powered by PhDFocusTM