A Multimodal Threat Detection Algorithm for Wide Area Network Security Based on Support Vector Machines
Year: 2025
Type: article
Abstract: Wide area networks (WANs) are increasingly susceptible to sophisticated cyber threats, particularly as critical infrastructure becomes more interconnected. For example, computing-first networks (CFNs) often traverse WANs at edge access nodes, making them more vulnerable to security threats. This paper proposes a multimodal threat detection framework that combines traffic statistics, system logs, and user behavior patterns to deliver interpretable and real-time classification of network threats. The system applies feature normalization and uses principal component analysis (PCA) to reduce dimensionality. A support vector machine (SVM) with a radial basis function kernel is then used to detect non-linear attack patterns. A web-based architecture enables real-time deployment via REST APIs, and extensive evaluations on the CICIDS 2017 and UNSW-NB15 datasets demonstrate high accuracy (up to 96.8%) and low-latency inference. Ablation studies confirm the importance of multimodal fusion, and benchmark tests validate scalability and system responsiveness. This work offers a deployable and efficient solution for real-time WAN security, with promising applications in energy systems, public infrastructure, and enterprise networks.
Source: Journal of Web Engineering
Author: Bo Yuan
Institution: Southeast University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414523706