DETECTING CYBER THREATS IN REAL-TIME: A NOVEL AUTOENCODER FRAMEWORK FOR NETWORK ANOMALY DETECTION
DOI:
https://doi.org/10.36676/jrps.v14.i5.1603Keywords:
Cybersecurity, Autoencoders, Real-time Detection, Network Anomaly Detection, DDoS Attacks, Smart Grid Networks, High-Performance Computing (HPC)Abstract
Current and emerging threats have gained in variety, frequency, and sophistication, which requires the formulation of heightened methods for identifying isolated behaviors and traffic patterns in the network. This paper presents an architecture for real-time cyber threat detection arising from network traffic using the autoencoder model. Mainly, real-life cases of traffic patterns are analyzed, and multiple simulations are provided to show that the proposed model would be efficient in reducing false positives and, at the same time, able to detect new changes in real-time. Lastly, we have graphical outputs for the performance metrics and the issues and recommendations regarding implementing such a system. These findings indicate that autoencoders are promising due to their ability to keep highly accurate detection while solving scalability and performance problems characteristic of real-time anomaly detection.
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