Simulation of Fine-Grained Network Intrusion Detection under Regularization Limit Learning
At present,the network communication data is generated quickly.In general,the fine-grained network intrusion detection system produces a large number of false positives,that is,mistakenly identifying normal network activities as abnormal behaviors,resulting in low accuracy of intrusion detection.In order to obtain high-precision and high-efficiency detection results of fine-grained network intrusion,this paper proposed a method of detecting fine-grained network intrusions based on regularized extreme learning.The method was divided into two stages.In the first stage,multiple sliding windows with different scales were used to divide the original network traffic into multiple sub-sequences of observation spans.Then,wavelet transform technology was used to reconstruct the subsequences,thus obtaining multi-level sequences.Moreover,the chain SAE used feature space mapping to form multi-level reconstruc-tion sequences.Based on the reconstruction sequence error,the preliminary determination of network traffic anomalies was carried out,and then the results of each level were summarized to realize the detection of network traffic anoma-lies.In the second stage,the regularized extreme learning weights and thresholds were jointly optimized by the beetle swarm algorithm(BSA).At the same time,the regularized extreme learning after optimization was used to perform fine-grained network intrusion detection on abnormal samples.Experimental results prove that the proposed method could accurately detect fine-grained network intrusions,with a detection rate of over 95%and a short detection time.
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