Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic

作者：

Highlights：

• Multiple attributes from IP flows are combined to detect anomalous events.

• GA metaheuristic used for Digital Signature of Network Segment using Flow Analysis.

• Unsupervised training technique applied efficiently for network traffic profiling.

• Fuzzy Logic improved accuracy and false positives compared to state of art.

摘要

•Multiple attributes from IP flows are combined to detect anomalous events.•GA metaheuristic used for Digital Signature of Network Segment using Flow Analysis.•Unsupervised training technique applied efficiently for network traffic profiling.•Fuzzy Logic improved accuracy and false positives compared to state of art.

论文关键词：Network management,Network Anomaly Detection System,Genetic Algorithm,Fuzzy Logic

论文评审过程：Received 6 December 2016, Revised 19 July 2017, Accepted 9 September 2017, Available online 23 September 2017, Version of Record 6 October 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.013