Network intrusion detection method combining longicorn stigma with random forest

Abstract

The invention relates to a network intrusion detection method combining longicorn stigma with a random forest, which is used for processing a network intrusion monitoring problem based on machine learning and can be used for training a network intrusion monitoring model with high accuracy by using less time. When abnormal flow passes, the detection system can find a problem through flow analysis and generate a corresponding signal. Compared with a particle swarm optimization random forest algorithm and a fruit fly optimization random forest algorithm, the method can complete model training anddetection more efficiently.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a network intrusion detection method combining beetles and random forests. Background technique [0002] With the wider popularization and application of the network, the risk of network intrusion has risen again, and the research on intrusion detection has become an indispensable part of the field of network security. At present, there have been certain research results in monitoring network traffic and building an effective intrusion detection system. Traditional network intrusion detection technologies include intrusion detection technology based on probability and statistics model, intrusion detection technology based on model reasoning, etc. The intrusion detection technology based on the probability and statistics model is the most traditional and basic intrusion detection technology. The disadvantage of this method is that the user behavior is various, and it can...

AI Technical Summary

Problems solved by technology

For parameter optimization problems, there are common fruit fly optimization algorithms and particle swarm optimization algorithms, but their calculations are huge and require a long calculation time

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