An Incremental Learning Approach with Support Vector Machine for Network Data Stream Classification Problem

Yao, Yuan; Feng, Lin; Jin, Bo; Feng, Chun-Mei

doi:10.3923/itj.2012.200.208

Cited by 7 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Memory, energy [79], and bandwidth are considered in the implementation of data processing on the sensors; for example, many summarization and aggregation techniques can be adopted to reduce energy and bandwidth consumption. The framework can address the problem quickly changing nature of WSNs data, where characteristics of the monitored process may change over time and render the old models outdated. This problem can be addressed using the incremental learning mechanism [39, 112] that helps the model to update new information. The framework can identified the spatial-temporal correlation at local model by using data correlation-based clustering, whereas attribute correlation can be identified at global model by using the multipass data mining algorithms.…”

Section: Future Research Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Data Mining Techniques for Wireless Sensor Networks: A Survey

Mahmood

Shi

Khatoon

et al. 2013

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Recently, data management and processing for wireless sensor networks (WSNs) has become a topic of active research in several fields of computer science, such as the distributed systems, the database systems, and the data mining. The main aim of deploying the WSNs-based applications is to make the real-time decision which has been proved to be very challenging due to the highly resource-constrained computing, communicating capacities, and huge volume of fast-changed data generated by WSNs. This challenge motivates the research community to explore novel data mining techniques dealing with extracting knowledge from large continuous arriving data from WSNs. Traditional data mining techniques are not directly applicable to WSNs due to the nature of sensor data, their special characteristics, and limitations of the WSNs. This work provides an overview of how traditional data mining algorithms are revised and improved to achieve good performance in a wireless sensor network environment. A comprehensive survey of existing data mining techniques and their multilevel classification scheme is presented. The taxonomy together with the comparative tables can be used as a guideline to select a technique suitable for the application at hand. Based on the limitations of the existing technique, an adaptive data mining framework of WSNs for future research is proposed.

show abstract

Section: Future Research Directionsmentioning

confidence: 99%

“…(iv) The framework can address the problem quickly changing nature of WSNs data, where characteristics of the monitored process may change over time and render the old models outdated. This problem can be addressed using the incremental learning mechanism [39,112] that helps the model to update new information.…”

Section: Future Research Directionsmentioning

confidence: 99%

Data Mining Techniques for Wireless Sensor Networks: A Survey

Mahmood

Shi

Khatoon

et al. 2013

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

Selection of Candidate Support Vectors in incremental SVM for network intrusion detection

Chitrakar

Huang

2014

Computers & Security

View full text Add to dashboard Cite

A comparative simulation of normalization methods for machine learning-based intrusion detection systems using KDD Cup’99 dataset

Kumar

Gupta

Arora

2022

IFS

View full text Add to dashboard Cite

Network Intrusion detection systems (NIDS) detect malicious and intrusive information in computer networks. Presently, commercial NIDS is based on machine learning approaches that have complex algorithms and increase intrusion detection efficiency and efficacy. These machine learning-based NIDS use high dimensional network traffic data from which intrusive information is to be detected. This high-dimensional network traffic data in NIDS needs to be preprocessed and normalized to make it suitable for machine learning tools. A machine learning approach with appropriate normalization and prepossessing increases NIDS performance. This paper presents an empirical study on various normalization methods implemented on a benchmark network traffic dataset, KDD Cup’99, that has been used to evaluate the NIDS model. The present study shows decimal normalization has a better prediction performance than non-normalized traffic data categorized into ‘normal’ or ‘intrusive’ classes.

show abstract

An Incremental Learning Approach with Support Vector Machine for Network Data Stream Classification Problem

Cited by 7 publications

References 16 publications

Data Mining Techniques for Wireless Sensor Networks: A Survey

Data Mining Techniques for Wireless Sensor Networks: A Survey

Selection of Candidate Support Vectors in incremental SVM for network intrusion detection

A comparative simulation of normalization methods for machine learning-based intrusion detection systems using KDD Cup’99 dataset

Contact Info

Product

Resources

About