Talaya Farasat
ABSTRACT
Forecasting network traffic is a challenging task for better network management. In this poster, we present a Border Gateway Protocol (BGP) traffic volume prediction framework that uses real BGP data from two famous Internet exchange points (IXPs) to train the LSTM network and generate future volume-based predictions. Our experimental evaluation shows that LSTM can indeed be used to predict BGP traffic volume with a very low prediction errors.
- 2021. RIPE. https://www.ripe.net/Google Scholar
- Bahaa Al-Musawi, Philip Branch, and Grenville Armitage. 2015. Detecting BGP Instability Using Recurrence Quantification Analysis (RQA). In IEEE International Performance Computing and Communications Conference (IPCCC). Nanjing, China.Google ScholarDigital Library
- Kevin Hoarau, Pierre Tournoux, and Tahiry Razafindralambo. 2021. BML: An Efficient and Versatile Tool for BGP Dataset Collection. In IEEE International Conference on Communications. Montreal, Canada.Google Scholar
- Nipun Ramakrishnan and Tarun Soni. 2018. Multi-Scale LSTM Model for BGP Anomaly Classification. In IEEE International Conference on Machine Learning and Applications. Orlando, FL, USA.Google Scholar
Index Terms
- BGP traffic volume forecasting using LSTM framework
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