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Traffic anomalies in communication networks greatly degrade network performance. Early detection of such anomalies alleviates their effect on network performance. A number of approaches that involve traffic modeling, signal processing, and machine learning techniques have been employed to detect network traffic anomalies. In this paper, we develop various Naive Bayes (NB) classifiers for detecting the Internet anomalies using the Routing Information Base (RIB) of the Border Gateway Protocoldoi:10.1109/icmlc.2012.6358901 dblp:conf/icmlc/Al-RousanHT12 fatcat:7s7ssvkqtrb6zdq7yyrc53yxua