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An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in streaming data

China Communications 2024年第5期21卷 249-260页

作者： R.Rajakumar S.Sathiya Devi Srinivasa Ramanujan Centre SASTRA Deemed UniversityKumbakonamTamil Nadu 612001India University College of Engineering BIT CampusAnna UniversityTiruchirappalli 620024India

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,*** recent developments of deep learning(DL)models find helpful in the detection and classification of *** article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)*** aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming *** proposed OS-ODLSDC model initially undergoes preprocessing *** streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling ***,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and ***,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM *** 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

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An Optimal Big data Analytics with Concept Drift Detection on High-Dimensional streaming data

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Computers, Materials & Continua 2021年第9期68卷 2843-2858页

作者： Romany F.Mansour Shaha Al-Otaibi Amal Al-Rasheed Hanan Aljuaid Irina V.Pustokhina Denis A.Pustokhin Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different *** is challenging to apply existing data mining tools and techniques directly in these big data *** the same time,streaming data from several applications results in two major problems such as class imbalance and concept *** current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional streaming *** presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and ***-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is ***,Statistical Test of Equal Proportions(STEPD),a CDD technique is also ***,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for *** order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried *** performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam *** extensive experimental validation process confirmed the effective outcome of MOMBD-CDD *** proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 dataset and ECUE Spam datasets respectively.

关键词： streaming data concept drift classification model deep learning class imbalance data

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An Online Visualization System for streaming Log data of Computing Clusters

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Tsinghua Science and Technology 2013年第2期18卷 196-205页

作者： Jing Xia Feiran Wu Fangzhou Guo Cong Xie Zhen Liu Wei Chen State Key Laboratory of CAD&CG Zhejiang University College of Computer Science and Technology Hangzhou Dianzi University

Monitoring a computing cluster requires collecting and understanding log data generated at the core, computer, and cluster levels at run time. Visualizing the log data of a computing cluster is a challenging problem due to the complexity of the underlying dataset： it is streaming, hierarchical, heterogeneous, and multi-sourced. This paper presents an integrated visualization system that employs a two-stage streaming process mode. Prior to the visual display of the multi-sourced information, the data generated from the clusters is gathered, cleaned, and modeled within a data processor. The visualization supported by a visual computing processor consists of a set of multivariate and time variant visualization techniques, including time sequence chart, treemap, and parallel coordinates. Novel techniques to illustrate the time tendency and abnormal status are also introduced. We demonstrate the effectiveness and scalability of the proposed system framework on a commodity cloud-computing platform.

关键词： computing cluster performance metrics monitoring streaming data visualization

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Fast counting the cardinality of flows for big traffic over sliding windows

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Frontiers of Computer Science 2017年第1期11卷 119-129页

作者： Jingsong SHAN Yinjin FU Guiqiang NI Jianxin LUO Zhaofeng WU College of Command Information Systems PLA University of Science and Technology Nanjing 223002 China Huaiyin Institute of Technology Huaian 221002 China

Counting the cardinality of flows for massive high-speed traffic over sliding windows is still a challenging work under time and space constrains, but plays a key role in many network applications, such as traffic management and routing optimization in software defined network. In this pa- per, we propose a novel data structure （called LRU-Sketch） to address the problem. The significant contributions are as follows. 1） The proposed data structure adapts a well-known probabilistic sketch to sliding window model; 2） By using the least-recently used （LRU） replacement policy, we design a highly time-efficient algorithm for timely forgetting stale information, which takes constant （O（1）） time per time slot; 3） Moreover, a further memory-reducing schema is given at a cost of very little loss of accuracy; 4） Comprehensive ex- periments, performed on two real IP trace files, confirm that the proposed schema attains high accuracy and high time *** including IEEE TPDS, ACM ToS, JCST, MIDDLEWARE, CLUSTER, NAS, etc. Currently, his research interests include big data management, cloud storage, and distributed file systems.

关键词： probabilistic data structure sketch streaming data cardinality flow

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数据流上的并行决策树构成算法

数据流上的并行决策树构成算法

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第二十二届中国数据库学术会议

作者：丁健李建中李金宝高宏黑龙江大学计算机科学技术学院哈尔滨工业大学计算机科学与技术学院

1引言决策树分类算法是数据挖掘中的一个重要问题。它与其它的分类方法比较起来,具有速度较快、较容易转换成简单的并且很容易理解的分类规则、也较容易转换成数据库查询语句、有时可得到更高准确度等优点。近年来,一种新的数据模型逐... 详细信息

1引言决策树分类算法是数据挖掘中的一个重要问题。它与其它的分类方法比较起来,具有速度较快、较容易转换成简单的并且很容易理解的分类规则、也较容易转换成数据库查询语句、有时可得到更高准确度等优点。近年来,一种新的数据模型逐渐引起了人们的注意,那就是持续不断的数据流。在数

关键词： Parallel decision tree construction streaming data Interval pruning Sample Gain function

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