An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data  

作　　者：R.Rajakumar S.Sathiya Devi 

机构地区：[1]Srinivasa Ramanujan Centre,SASTRA Deemed University,Kumbakonam,Tamil Nadu 612001,India [2]University College of Engineering,BIT Campus,Anna University,Tiruchirappalli 620024,India

出　　处：《China Communications》2024年第5期249-260,共12页中国通信（英文版）

摘　　要：Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

关 键 词：anomaly detection deep learning hyperparameter optimization OVERSAMPLING SMOTE streaming data 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN915.08[自动化与计算机技术—控制科学与工程] TP391.44[电子电信—通信与信息系统]