Enhancing Cybersecurity through the Analysis of BGP Protocol for Detecting and Classifying Security Threats

Abstract

The scrutiny and identification of anomalies within the Border Gateway Protocol (BGP) stand as pivotal focal points in contemporary cybersecurity research. This paper navigates this intricate terrain, exploring diverse anomaly detection methodologies, including and historical-based analyses, and machine learning applications, all applied to comprehensive BGP datasets. Drawing from BGP update messages sourced from Reseaux IP Européens and Route Views, the study specifically investigates anomalies induced by the Moscow blackout.
The research unveils insights into the dynamic landscape of BGP anomalies, shedding light on the impact and characteristics of incidents caused by specific threats. Leveraging real-world datasets enhances the authenticity of the analysis, contributing to a nuanced understanding of the vulnerabilities within the BGP protocol. By the Moscow blackout, this paper offers a tangible and contextualized exploration of BGP anomalies, advancing our comprehension of cybersecurity threats and fortifications.
Furthermore, the paper proposes enhancements and solutions aimed at fortifying the BGP protocol against emerging threats. The evaluation and validation section critically assesses the proposed solutions, offering insights into their practical applicability and efficacy. The discussion section contextualizes the findings within the broader realm of cybersecurity, emphasizing the significance of proactive measures in mitigating potential risks. In conclusion, this research contributes valuable insights into the evolving landscape of cybersecurity, offering tangible enhancements to fortify BGP against emerging threats.