Unsupervised learning clustering and self-organized agents applied to help network management
作者：
Highlights：
• Self-organized agents use multidimensional flow analysis to help network management.
• Traffic profiling and anomaly detection tasks are designed to operate autonomously.
• Reports are provided in real time to aid decision-making when anomalous events occur.
• A pattern matching technique calculates adaptive thresholds for anomaly detection.
• False alarm and accuracy rates are encouraging both in real and simulated traffic.
摘要
•Self-organized agents use multidimensional flow analysis to help network management.•Traffic profiling and anomaly detection tasks are designed to operate autonomously.•Reports are provided in real time to aid decision-making when anomalous events occur.•A pattern matching technique calculates adaptive thresholds for anomaly detection.•False alarm and accuracy rates are encouraging both in real and simulated traffic.
论文关键词：Ant Colony Optimization,Traffic characterization,Network management,Unsupervised learning,Anomaly detection,Self-organized agents
论文评审过程：Received 23 November 2014, Revised 2 January 2016, Accepted 3 January 2016, Available online 29 January 2016, Version of Record 13 February 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.01.032
