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...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.展开更多
Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approach...Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classification problems have received consid-erable interest.At the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes important.This study develops an outlier detec-tion with feature selection technique for streaming data classification,named ODFST-SDC technique.Initially,streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition,Local Correla-tion Integral(LOCI)is used which is significant in the detection and removal of outliers.Besides,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of features.Finally,kernel extreme learning machine(KELM)classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.展开更多
文摘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.
文摘Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classification problems have received consid-erable interest.At the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes important.This study develops an outlier detec-tion with feature selection technique for streaming data classification,named ODFST-SDC technique.Initially,streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition,Local Correla-tion Integral(LOCI)is used which is significant in the detection and removal of outliers.Besides,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of features.Finally,kernel extreme learning machine(KELM)classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.
