{"title":"An efficient modelling of oversampling with optimal deep learning enabled anomaly detection in streaming data","authors":"R. Rajakumar, S. S. Devi","doi":"10.23919/JCC.ja.2022-0592","DOIUrl":null,"url":null,"abstract":"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 OS-ODLSDC 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 (BiLSTM) for AD and classification. Finally, the root means square propagation (RMSProp) optimizer is applied for optimal hyperparameter tuning of the BiL-STM 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.","PeriodicalId":9814,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"China Communications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.23919/JCC.ja.2022-0592","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
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 OS-ODLSDC 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 (BiLSTM) for AD and classification. Finally, the root means square propagation (RMSProp) optimizer is applied for optimal hyperparameter tuning of the BiL-STM 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.
期刊介绍:
China Communications (ISSN 1673-5447) is an English-language monthly journal cosponsored by the China Institute of Communications (CIC) and IEEE Communications Society (IEEE ComSoc). It is aimed at readers in industry, universities, research and development organizations, and government agencies in the field of Information and Communications Technologies (ICTs) worldwide.
The journal's main objective is to promote academic exchange in the ICTs sector and publish high-quality papers to contribute to the global ICTs industry. It provides instant access to the latest articles and papers, presenting leading-edge research achievements, tutorial overviews, and descriptions of significant practical applications of technology.
China Communications has been indexed in SCIE (Science Citation Index-Expanded) since January 2007. Additionally, all articles have been available in the IEEE Xplore digital library since January 2013.