An Improved KNN-Based Efficient Log Anomaly Detection Method with Automatically Labeled Samples

Author:

Ying Shi1,Wang Bingming1,Wang Lu2,Li Qingshan2,Zhao Yishi3,Shang Jianga3,Huang Hao1,Cheng Guoli1,Yang Zhe1,Geng Jiangyi1

Affiliation:

1. Wuhan University, HuBei, China

2. Xidian University, XiAn, China

3. China University of Geosciences, Wuhan, China

Abstract

Logs that record system abnormal states (anomaly logs) can be regarded as outliers, and the k-Nearest Neighbor (kNN) algorithm has relatively high accuracy in outlier detection methods. Therefore, we use the kNN algorithm to detect anomalies in the log data. However, there are some problems when using the kNN algorithm to detect anomalies, three of which are: excessive vector dimension leads to inefficient kNN algorithm, unlabeled log data cannot support the kNN algorithm, and the imbalance of the number of log data distorts the classification decision of kNN algorithm. In order to solve these three problems, we propose an efficient log anomaly detection method based on an improved kNN algorithm with an automatically labeled sample set. This method first proposes a log parsing method based on N-gram and frequent pattern mining (FPM) method, which reduces the dimension of the log vector converted with Term frequency.Inverse Document Frequency (TF-IDF) technology. Then we use clustering and self-training method to get labeled log data sample set from historical logs automatically. Finally, we improve the kNN algorithm using average weighting technology, which improves the accuracy of the kNN algorithm on unbalanced samples. The method in this article is validated on six log datasets with different types.

Funder

National Natural Science Foundation of China

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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