An efficient modelling of oversampling with optimal deep learning enabled anomaly detection in streaming data

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.