Multi-Threaded Routing Toolkit (MRT) Border Gateway Protocol (BGP) Routing Information Export Format with Geo-Location Extensions

Manderson, Terry

doi:10.17487/rfc6397

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…The interval between consecutive messages was later decreased to five minutes. BGP update messages are collected by the Remote Route Collectors (RRCs) and stored in the multi-threaded routing toolkit (MRT) format [11].…”

Section: Bgp Datamentioning

confidence: 99%

Performance evaluation of BGP anomaly classifiers

Ćosović

Obradović

Trajković

2015

2015 Third International Conference on Digital Information, Networking, and Wireless Communications (DINWC)

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Abstract-Changes in the network topology such as large-scale power outages or Internet worm attacks are events that may induce routing information updates. Border Gateway Protocol (BGP) is by Autonomous Systems (ASes) to address these changes. Network reachability information, contained in BGP update messages, is stored in the Routing Information Base (RIB). Recent BGP anomaly detection systems employ machine learning techniques to mine network data. In this paper, we evaluated performance of several machine learning algorithms for detecting Internet anomalies using RIB. Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (J48) classifiers are employed to detect network traffic anomalies. We evaluated feature discretization and feature selection using three data sets of known Internet anomalies.

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Section: Bgp Datamentioning

confidence: 99%

Performance evaluation of BGP anomaly classifiers

Ćosović

Obradović

Trajković

2015

2015 Third International Conference on Digital Information, Networking, and Wireless Communications (DINWC)

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“…We are interested in BGP update messages since they carry network reachability information between autonomous systems that in times of intentional or unintentional changes in network topology respond with an increased number of routing updates. Update messages are stored in MRT [13] format and are transformed to ASCII by using the bgpdump library written in C and maintained by the NCC. We load BGP update messages of known network anomalies into a database, and by using SQL queries we extract features that are of interest to us.…”

Section: Bgp Datamentioning

confidence: 99%

BGP Anomaly Detection with Balanced Datasets

Ćosović

Obradović

2018

Teh. vjesn.

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We use machine learning techniques to build predictive models for anomaly detection in the Border Gateway Protocol (BGP). Imbalanced datasets of network anomalies pose limitations to building predictive models for anomaly detection. In order to achieve better classification performance measures, we use resampling methods to balance classes in the datasets. We use undersampling, oversampling and combination techniques to change class distributions of the datasets. In this paper we build predictive models based on preprocessed network anomaly datasets of known Internet network anomalies and observe improvement in classifier performance measures compared to those reported in our previous work. We propose to use resampling combination techniques on datasets along with Decision Tree and Naïve Bayes classifiers in order to achieve the best trade-off between (1) the F-measure and the length of model training time, and (2) avoiding overfitting and loss of information.

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“…The RIPE and Route Views [11] BGP update messages are available to the research community in the multi-threaded routing toolkit (MRT) binary format [12]. The Internet Engineering Task Force (IETF) introduced MRT to export routing protocol messages, state changes, and content of the routing information base (RIB).…”

Section: Understanding Bgp Datamentioning

confidence: 99%

Classification of BGP anomalies using decision trees and fuzzy rough sets

Xing

Hua

et al. 2014

2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Border Gateway Protocol (BGP) is the core component of the Internet's routing infrastructure. Abnormal routing behavior impairs global Internet connectivity and stability. Hence, designing and implementing anomaly detection algorithms is important for improving performance of routing protocols. While various machine learning techniques may be employed to detect BGP anomalies, their performance strongly depends on the employed learning algorithms. These techniques have multiple variants that often work well for detecting a particular anomaly. In this paper, we use the decision tree and fuzzy rough set methods for feature selection. Decision tree and extreme learning machine classification techniques are then used to maximize the accuracy of detecting BGP anomalies. The proposed techniques are tested using Internet traffic traces.

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Multi-Threaded Routing Toolkit (MRT) Border Gateway Protocol (BGP) Routing Information Export Format with Geo-Location Extensions

Abstract: This document updates the Multi-threaded Routing Toolkit (MRT) export format for Border Gateway Protocol (BGP) routing information by extending it to include optional terrestrial coordinates of a BGP collector and its BGP peers.

Cited by 9 publications

References 6 publications

Performance evaluation of BGP anomaly classifiers

Performance evaluation of BGP anomaly classifiers

BGP Anomaly Detection with Balanced Datasets

Classification of BGP anomalies using decision trees and fuzzy rough sets

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