BGP Anomaly Detection using Decision Tree Based Machine Learning Classifiers
Anisha Bhatnagar1, Namrata Majumdar2, Shipra Shukla3
1Anisha Bhatnagar, Amity School of Engineering and Technology, Amity University, Noida, India. Email:
2Namrata Majumdar, Amity School of Engineering and Technology, Amity University, Noida, India.
3Shipra Shukla*, Assistant Professor, Amity School of Engineering and Technology, Amity University, Noida, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4015-4020 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36221081219/2019©BEIESP | DOI: 10.35940/ijitee.L3622.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Border Gateway Protocol (BGP) is utilized to send and receive data packets over the internet. Over the years, this protocol has suffered from some massive hits, caused by worms, such as Nimda, Slammer, Code Red etc., hardware failures, and/or prefix hijacking. This caused obstruction of services to many. However, Identification of anomalous messages traversing over BGP allows discovering of such attacks in time. In this paper, a Machine Learning approach has been applied to identify such BGP messages. Principal Component Analysis technique was applied for reducing dimensionality up to 2 components, followed by generation of Decision Tree, Random Forest, AdaBoost and GradientBoosting classifiers. On fine tuning the parameters, the random forest classifier generated an accuracy of 97.84%, the decision tree classifier followed closely with an accuracy of 97.38%. The GradientBoosting Classifier gave an accuracy of 95.41% and the AdaBoost Classifier gave an accuracy of 94.43%.
Keywords: Anomalies, Border Gateway Protocol (BGP), Decision Trees, Machine Learning (ML)
Scope of the Article: Machine Learning