Advancing Network Anomaly Detection: An Ensemble Approach Combining Optimized Contractive Autoencoders and K - Means Clustering

This paper introduces a new approach for detecting unusual activities in network traffic, a critical aspect in main-taining network security. We propose an innovative model that combines the strengths of Contractive Autoencoders (CAEs) and K-means clustering, specifically designed for effective anomaly detection in network environments. Our model employs CAEs for efficient data processing and K-means clustering to identify deviations from standard network patterns. The focus is on the exploration of CAE's latent space and the impact of various deep learning parameters on the model's detection capabilities. Tested on the NSL-KDD dataset, a standard in network security research, our best-tuned model achieves an F1 Score of 0.92, making it approximately 8.2 % more effective than the basic Autoencoder model and about 5.7% better than the standalone K-Means approach in terms of F1 Score. This significant improvement in performance highlights the advanced capabilities of our model in identifying potential threats in network traffic, marking a considerable advancement in the field of network security.