Detecting Overlapping Data in System Logs Based on Ensemble Learning Method

Abstract

Machine learning techniques are essential for system log anomaly detection. It is prone to the phenomenon of class overlap because of too many similar system log data. The occurrence of this phenomenon will have a serious impact on the anomaly detection of the system logs. To solve the problem of class overlap in system logs, this paper proposes an anomaly detection model for class overlap problem on system logs. We first calculate the relationship between the sample data and the membership of different classes, normal or anomaly, and use the fuzziness to separate the sample data of the overlapping parts of the classes from the data of the other parts. AdaBoost, an ensemble learning approach, is used to detect overlapping data. Compared with machine learning algorithms, ensemble learning can better classify the data of the overlapping parts, so as to achieve the purpose of detecting the anomalies of the system logs. We also discussed the possible impact of different voting methods on ensemble learning results. Experimental results show that our model can be effectively applied in a variety of basic algorithms, and the results of each measure have been improved.