TY - GEN
T1 - Classification of BGP anomalies using decision trees and fuzzy rough sets
AU - LI, Yan
AU - XING, Hong-Jie
AU - HUA, Qiang
AU - WANG, Xi-Zhao
AU - BATTA, Prerna
AU - HAERI, Soroush
AU - TRAJKOVÍC, Ljiljana
N1 - This research was supported by the China Scholarship Council and the Natural Sciences and Engineering Research Council of Canada Grant 216844-13.
PY - 2014/1
Y1 - 2014/1
N2 - 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.
AB - 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.
KW - Decision tree
KW - Extreme learning machine
KW - Fuzzy rough sets
KW - Machine learning
KW - Weighted extreme learning machine
UR - http://www.scopus.com/inward/record.url?scp=84938090079&partnerID=8YFLogxK
U2 - 10.1109/SMC.2014.6974096
DO - 10.1109/SMC.2014.6974096
M3 - Conference paper (refereed)
AN - SCOPUS:84938090079
T3 - IEEE International Conference on Systems, Man and Cybernetics
SP - 1312
EP - 1317
BT - Proceedings : 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
PB - IEEE
T2 - 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
Y2 - 5 October 2014 through 8 October 2014
ER -