Statistical Analysis and Advanced Predictive Techniques to Optimize Detection of Network Traffic Anomaly and Improve Network Security and Threat Identification

Abstract

Traffic patterns in today’s network environment vary considerably, and traditional anomaly detection methods were not designed to address the various issues relating to the high dimensionality of structural representations of network topologies, temporal dependencies, and real-time response, leading to decreased accuracy and consistency in identifying anomalies. The research presented in this paper proposes a novel method of detecting anomalies within network traffic utilizing statistical analysis optimized by means of advanced predictive techniques. Using the structured features of cleaned-up network traffic datasets, the study creates a statistical feature analysis approach in order to develop a baseline of normal network traffic behaviour; this provides insight into the statistical nature of your organisation’s network traffic patterns. In addition, the paper utilizes an LSTM and a multi-level predictive model to develop a methodology to predict the future behaviour of the network traffic. Finally, by leveraging the residual-based anomaly scoring techniques, experiments demonstrate the ability to support accurate and timely identification of network traffic anomalies. The experiments were conducted on many different types of network traffic datasets and resulted in an anomaly detection accuracy of 96.8%, an F1 score of 95.9%, a false positive rate of 0.02, a false negative rate of 0.11, and an average residual prediction convergence rate of 0.0125 during training. The median time required for the detection of anomalies end-to-end was 0.20 seconds. The results presented in this paper verify that the proposed methodology has the potential for very precise and low-latency detection of network traffic anomalies and will provide technical support for the identification of network security and network threat risks.

Keywords: Network traffic anomaly detection; advanced prediction techniques; statistical analysis; network security; threat identification

Cite As

Z. Zhang, X. Liu, "Statistical Analysis and Advanced Predictive Techniques to Optimize Detection of Network Traffic Anomaly and Improve Network Security and Threat Identification", Engineering Intelligent Systems, vol. 35 no. 5, pp. 665-681, 2026.