Anomaly Detection in Real-Time Network Traffic Using AI-Powered Streaming Analytics Frameworks
Keywords:
Anomaly Detection, Network Traffic, Real-Time AnalyticsAbstract
The need for real-time anomaly detection in network traffic is critical for maintaining the integrity and security of modern IT infrastructures. With increasing network complexities and the rise of cyber threats, traditional methods of network monitoring often fall short in providing timely and accurate insights. This paper explores the role of AI-powered streaming analytics frameworks in detecting anomalies in real-time network traffic. By leveraging advanced machine learning algorithms, these frameworks can identify suspicious activities, unauthorized access, and potential network attacks with greater efficiency. This research delves into the architecture of these frameworks, the machine learning models used for anomaly detection, and their performance in real-world applications. Key challenges such as data volume, feature selection, and false positive rates are also discussed, along with potential solutions. Ultimately, the paper highlights the promise of AI-driven approaches to transform network security by enabling proactive defense mechanisms that can adapt to evolving threats.
Downloads
References
Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19-31. https://doi.org/10.1016/j.jnca.2015.11.015
Carcillo, F., & Michiels, S. (2020). Real-time anomaly detection in network traffic using machine learning models. International Journal of Computer Applications, 175(7), 45-53.
Ali, S. A. "OPENSTACK AND OVN INTEGRATION: EXPLORING THE ARCHITECTURE, BENEFITS, AND FUTURE OF VIRTUALIZED NETWORKING IN CLOUD ENVIRONMENTS." INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 1.4 (2017): 34-65.
Zhang, Q., & Zhao, X. (2018). Deep learning for anomaly detection in network traffic. Proceedings of the IEEE International Conference on Cybersecurity, 22-29.
Kim, S., & Lee, J. (2019). Anomaly detection in high-velocity network traffic using streaming machine learning models. Journal of Cloud Computing, 8(2), 135-149.
Sharma, P., & Pandey, P. (2021). Feature selection in network anomaly detection using ensemble methods. Computational Intelligence, 37(3), 671-688.
Ahmed, M., & Soni, S. (2019). Reducing false positives in real-time anomaly detection. Journal of Cybersecurity Research, 13(1), 45-59.
Gupta, R., & Singh, H. (2021). Performance evaluation of machine learning models for network anomaly detection. Journal of Computer Networks and Communications, 2021, 1-14.
Liu, Z., & Zhang, Y. (2020). Reinforcement learning in real-time network traffic analysis. IEEE Transactions on Network and Service Management, 17(4), 3072-3087.
Wiegand, P., & Lange, M. (2020). Explainable AI for anomaly detection in network security. Proceedings of the 2020 International Workshop on AI Security, 10-20.
Zhou, H., & Kim, H. (2019). Detecting unknown attacks in network traffic using deep learning models. Proceedings of the 2019 International Conference on Network Security, 73-80.
Jin, C., & Wei, W. (2021). Hybrid models for real-time network anomaly detection. Journal of Computational Intelligence in Engineering, 15(2), 1-9.
Sun, Y., & Li, J. (2020). Anomaly detection for IoT networks using AI-powered analytics. International Journal of Internet Technology and Secured Transactions, 11(4), 275-289.
Liu, J., & Zhang, W. (2020). A comprehensive review of anomaly detection algorithms in cybersecurity. IEEE Access, 8, 73897-73910.
Li, X., & Chen, Z. (2019). Detecting DDoS attacks in real-time network traffic using machine learning. Journal of Cyber Security Technology, 3(1), 1-18.
Yang, X., & Liu, X. (2021). Real-time anomaly detection in network traffic using unsupervised machine learning techniques. Computational Security and Privacy Journal, 7(2), 113-126.
Kumar, R., & Sharma, A. (2020). Network anomaly detection using deep learning models: A survey. International Journal of Applied Artificial Intelligence, 34(3), 215-226.
Zhang, H., & Zhou, Q. (2020). Enhancing real-time anomaly detection with hybrid machine learning approaches. Journal of Network and Systems Management, 28(5), 1354-1367.
Wang, H., & Zhai, S. (2021). Detecting network intrusions using streaming analytics and machine learning. IEEE Transactions on Network and Service Management, 18(2), 420-430.
Yang, Q., & Li, X. (2018). Stream-based anomaly detection in high-speed networks. Journal of Computational Methods in Cybersecurity, 12(4), 134-147.
He, H., & Wang, F. (2020). A novel AI-based approach for network traffic anomaly detection. IEEE Transactions on Network and Service Management, 17(4), 1572-1585.
