Clusters in chaos: A deep unsupervised learning paradigm for network anomaly detection

Abstract

In response to the escalating sophistication of cyber threats, traditional security measures are proving insufficient, necessitating advanced solutions. The complexity of cyberattacks renders standard protocols inadequate, leading to an increased frequency of disruptions, data breaches, and financial losses. To address aforementioned challenges, a novel deep clustering algorithm developed to handle high-dimensional network data. Furthermore, the suggested autoencoder method improves anomaly detection by enabling a threshold value. The integration of clustering and the autoencoder method effectively handles anomaly detection. More specifically, involving the grouping of similar normal data points through clustering, followed by training individual autoencoders for each cluster. This innovative technique captures nuanced patterns of normal behavior within each cluster, significantly enhancing the model’s ability to detect anomalies. In addition to implement the intelligent system, NSL-KDD dataset is considered. From the simulation results, the proposed Cluster Autoencoder Pair (CAEP) model reveals that the overall accuracy of 96%, precision of 97%, recall of 98%, and F1-score of 97%, demonstrating superior performance compared to other existing models for network anomaly detection.