Apr 21, 2021 · We propose an efficient log anomaly detection method based on an improved kNN algorithm with an automatically labeled sample set.
We propose an efficient log anomaly detection method based on an improved kNN algorithm with an automatically labeled sample set.
It mainly consists of three steps: parsing and vectorization of log data, automatic construction of data sample set with labels, and anomaly detection with the ...
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 ...
Finally, we improve the kNN algorithm using average weighting technology, which improves the accuracy of the kNN algorithm on unbalanced samples. The method in ...
In this paper, we propose a log-based anomaly detection method with efficient selection of neighbors and automatic selection of k neighbors.
An Improved KNN-Based Efficient Log Anomaly Detection Method with ...
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Logs that record system abnormal states (anomaly logs) can be regarded as outliers, and the k-Nearest Neighbor (kNN) ...
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Thus, we propose an improved KNN algorithm-based method which uses the existing mean-shift clustering algorithm to efficiently select the training set from ...
Thus, we propose an improved KNN algorithm-based method which uses the existing mean-shift clustering algorithm to efficiently select the training set from ...
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This paper presents an improved, modified KNN classifier using clustering optimization which is more effective at curbing both known and known intrusions in ...
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