Network traffic anomaly detection algorithm using mahout classifier

Abstract

 Now, the classifier network anomaly traffic detection method is generally considered to be a general method of good detection effect and high detection precision. In order to detect abnormal network traffic more efficiently, so as to ensure the security of Internet users, a network traffic anomaly detection algorithm based on Mahout Classifier is studied. Aiming at the problem of time correlation and the influence of abnormal samples on accuracy of detection statistics, and the first detection point is eliminated and the applicable detection point is added. The BP neural network algorithm and Bayesian network model are used to predict the abnormal probability of the anomaly node, the detection precision is optimized, and the exception points of the training set are reorganized. In view of the anomalies detected by anomaly detection models, an emergency response method is proposed, which not only detects anomalies, but also handles anomalies.