Year: 2025
Type: article
Abstract: Recently, network security incidents have been increasing as the Internet continues to expand. One such incident, BGP hijacking, has gained attention because it can cause major disruptions to Autonomous Systems (AS) through network attacks. Previous studies on detecting BGP hijacking have focused on analyzing BGP messages collected from a single router and identifying incidents using a Multi‐Layer Perceptron (MLP). However, this approach is limited and cannot detect incidents caused by organizations other than ISPs. To overcome this limitation, we propose a new method that uses BGP messages collected from multiple measurement points and applies advanced machine learning techniques to improve detection accuracy. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
DOI: 10.1002/tee.70058
Authors: Haruto Shimizu, Kimihiro Mizutani
Institution: Kindai University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,900
OpenAlex ID: W4410190016