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.
Highlights
With the emergence of cloud computing, integration of three networks, Internet of Things and mobile internet, the massive growth of data and the ever-changing types of data indicate that we have entered the era of big data
In order to meet the needs of daily network patrol work, a large number of intelligent electronic patrol systems have emerged in a large number of enterprise network management systems
In order to improve the accuracy and efficiency of network anomaly detection, this paper proposes a method of combining comprehensive network element performance index with system log data [8]
Summary
With the emergence of cloud computing, integration of three networks, Internet of Things and mobile internet, the massive growth of data and the ever-changing types of data indicate that we have entered the era of big data. How to find problems efficiently and timely, eliminate hidden dangers and prevent accidents. With the rapid development of network technology, patrol work is to maintain the safe and stable operation of network of all walks of life, and the safety and stability of the industry are very important, so the network anomaly detection is necessary. Failures and hidden dangers are generally reported by telephone, and the patrol records are mainly managed by hand, so the traditional patrol method lacks the real-time tracking and monitoring of patrol, and quantitative assessment of patrol maintenance quality. This paper proposes to complete mining of log data and extract features through the big data analysis technology, comprehensively analyze network element performance data, use the time series analysis algorithm to discover network anomalies, and improve the network security performance
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