Using Linear Discriminant Analysis for Dimensionality Reduction for Predicting Anomalies of BGP data
Namrata Majumdar1, Anisha Bhatnagar2, Shipra Shukla3
1Namrata Majumdar, Amity School of Engineering and Technology, Amity University, Noida, India.
2Anisha Bhatnagar, Amity School of Engineering and Technology, Amity University, Noida, India.
3Shipra Shukla, Amity School of Engineering and Technology, Amity University, Noida, India.
Manuscript received on 27 August 2019. | Revised Manuscript received on 07 September 2019. | Manuscript published on 30 September 2019. | PP: 1989-1995 | Volume-8 Issue-11, September 2019. | Retrieval Number: K21590981119/2019©BEIESP | DOI: 10.35940/ijitee.K2159.0981119
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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 a vital protocol on the internet for transfer of data packets among Autonomous System (AS). Security is a major concern for the transmission of BGP packets which are often attacked by worms or are hijacked by an attacker which results in requests entering black holes or loss of connection to the particular sites. The BGP anomalies can be reduced by analyzing the BGP datasets. Since, ASes communicate through messages, therefore, the anomalies can be reduced by identifying the corrupted BGP message in the dataset. In this paper, BGP anomalies have been classified by applying Machine learning (ML) algorithms. The dataset contains information about the sending and receiving time between ASes. The classifiers were used to predict the anomalies. Since the dataset had high dimensions, the dimensions were reduced using Linear Discriminant Analysis (LDA) and then Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Linear Regression, Logistic Regression and Multi-Layer Perceptron (MLP) have been used to classify the anomalies.
Keywords: Anomalies, BGP, Linear Discriminant Analysis, Machine Learning
Scope of the Article: Machine Learning