Sandford, P.J., Parish, D.J. and Sandford, J.M. (2006) Detecting Security Threats in the Network Core Using Data Mining Techniques. Network Operations & Management Symposium, Vancouver, 3-7 April 2006, 1-4.
https://doi.org/10.1109/NOMS.2006.1687640

has been cited by the following article:

• TITLE: Research on the Anomaly Detection Method in Intelligent Patrol Based on Big Data Analysis

  AUTHORS: Xiaoqing Deng

  KEYWORDS: Big Data, Intelligent Patrol, Anomaly Detection

  JOURNAL NAME: Journal of Computer and Communications, Vol.7 No.8, August 21, 2019

  ABSTRACT: The network anomaly detection in intelligent patrol is based on the trigger of a single threshold of network element performance parameters in patrol task, which has a high false alarm rate and low efficiency. In order to effectively and accurately integrate network performance, this paper proposes to mine network element performance data and network element log information in the integrated automatic patrol to detect network anomalies. Because log files have a large amount of data and a variety of types, and log data has a complex structure and contains large implied information. The relationship between network anomalies and time can actively discover through the analysis of the log files. Therefore, big data mining and classification can greatly improve the efficiency of data processing. However, the accuracy of finding network anomalies is insufficient only for log analysis. Therefore, this paper puts forward the performance indexes collected in the log analysis and patrol inspection system and adopts the sequence analysis algorithm to detect network anomalies, so as to improve the accuracy and efficiency of detection.