Lasso-based anomaly detection method and system

Abstract

The invention provides a Lasso (Least absolute shrinkage and selectionator operator)-based anomaly detection method and system. The method comprises the steps of establishing an anomaly detection model; determining model parameters through a Lasso algorithm; inputting to-be-detected data and obtaining a predicted value; comparing the predicted value with a preset threshold; and judging whether anomaly data exists or not. According to the method and system, the accuracy of judging a network anomaly behavior is improved on the basis of ensuring detection speed in combination with excellent characteristics of quick parameter estimation and accurate regression fitting of an Lasso; a sparse representation method is used in a data processing process, so that data dimensions are greatly reduced, model detection time is shortened, higher detection speed is achieved, and real-time online detection can be realized; and network data and host data can be both monitored, the data can be processed in batches in a matrix form, and hardware is adopted for realizing a linear regression method, so that the algorithm execution speed is greatly increased and quick, efficient and accurate anomaly detection is realized.

Description

technical field [0001] The invention relates to the field of statistical analysis, in particular to a Lasso-based anomaly detection method and system. Background technique [0002] Mathematical statistics is a branch of mathematics developed along with the development of probability theory. It studies how to effectively collect, organize and analyze data affected by random factors, and make inferences or predictions about the issues under consideration, in order to take certain decisions and At present, the use of mathematical statistical models to effectively mine information from massive data has attracted more and more attention from the industry. At the beginning of building the model, in order to minimize the model deviation due to the lack of important independent variables, usually choose As many independent variables as possible, however, the modeling process needs to find the set of independent variables that have the strongest explanatory power for the dependent va...

AI Technical Summary

Problems solved by technology

If the Bayesian statistical model is used, the parameter learning time will be longer, and the accuracy will be easily affected by the training set.

However, inspection methods based on single optimization parameters such as distance-based and density-based inspection methods have fast detection speed and short convergence time, but their detection accuracy cannot be guaranteed. Therefore, a new anomaly detection method is urgently needed. to improve the detection accuracy

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