In response to the problems of low recognition rate and high packet loss rate in existing network abnormal traffic detection meth-ods,research is conducted on abnormal traffic monitoring technology based on behavioral profiling data mining algorithms.It processes the collected traffic data through behavior profiling data mining,deletes redundant data to map effective feature data,and obtains a new feature dataset through fusion calculation.It also sets an abnormal behavior threshold,introduces exposure pat-terns to establish abnormal rules,and achieves real-time monitoring of abnormal traffic based on changes in the threshold.The ex-perimental results show that in the face of network attacks,the proposed anomaly traffic monitoring technology based on behav-ior profiling data mining still maintains a high level of recognition accuracy,and the data packet loss rate is low,indicating that the security of this monitoring technology has been improved.
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