Deep Learning Approaches for Anomaly Detection in Network Traffic: A Comparative Study

Professor Sarah Patel

Abstract


 Anomaly detection plays a critical role in maintaining the security and integrity of network systems. This paper conducts a comparative study of deep learning approaches for anomaly detection in network traffic. We evaluate the performance of various neural network architectures and anomaly detection algorithms, providing insights into their effectiveness, scalability, and applicability in real-world network environments.

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