Author Identifier
Ben A. Scott: https://orcid.org/0000-0003-3209-2184
Michael N. Johnstone: https://orcid.org/0000-0001-7192-7098
Patryk Szewczyk: https://orcid.org/0000-0003-3040-9344
Document Type
Journal Article
Publication Title
Sensors (Basel, Switzerland)
Volume
24
Issue
19
PubMed ID
39409453
Publisher
MDPI
School
School of Science
RAS ID
72491
Funders
Edith Cowan University / Cyber Security Research Centre Limited / Australian Government’s Cooperative Research Centres Programme
Abstract
The Internet's default inter-domain routing system, the Border Gateway Protocol (BGP), remains insecure. Detection techniques are dominated by approaches that involve large numbers of features, parameters, domain-specific tuning, and training, often contributing to an unacceptable computational cost. Efforts to detect anomalous activity in the BGP have been almost exclusively focused on single observable monitoring points and Autonomous Systems (ASs). BGP attacks can exploit and evade these limitations. In this paper, we review and evaluate categories of BGP attacks based on their complexity. Previously identified next-generation BGP detection techniques remain incapable of detecting advanced attacks that exploit single observable detection approaches and those designed to evade public routing monitor infrastructures. Advanced BGP attack detection requires lightweight, rapid capabilities with the capacity to quantify group-level multi-viewpoint interactions, dynamics, and information. We term this approach advanced BGP anomaly detection. This survey evaluates 178 anomaly detection techniques and identifies which are candidates for advanced attack anomaly detection. Preliminary findings from an exploratory investigation of advanced BGP attack candidates are also reported.
DOI
10.3390/s24196414
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Scott, B. A., Johnstone, M. N., & Szewczyk, P. (2024). A Survey of Advanced Border Gateway Protocol Attack Detection Techniques. Sensors, 24(19). https://doi.org/10.3390/s24196414