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Multimedia Tools and Applications

, Volume 74, Issue 16, pp 6391–6411 | Cite as

Web-based monitoring approach for network-based intrusion detection and prevention

  • Naruemon WattanapongsakornEmail author
  • Chalermpol Charnsripinyo
Article

Abstract

There were many reports about incidents of network attacks and security treats. Damages caused by network attacks and malwares can be extremely expensive or unaffordable. In this paper, we present a web-based management system for network-based intrusion detection and prevention. Users can get access from any mobile devices to see current network status, if there is an incident of network attack in the network environment. Our intrusion detection and prevention systems (IDPS) can be applied with different well-known detection algorithms which are C4.5 Decision Tree, Random Forest, Ripple Rule, Bayesian Network, Back-Propagation Neural Network. These algorithms can give very high detection accuracy for known attacks, where the attack type was previously trained/ learnt by the system. However, when new or unfamiliar/unknown attacks are encountered, the algorithms do not perform well. So, we develop a new detection technique based on Fuzzy Genetic Algorithm (Fuzzy GA) to handle the problem. Our IDPS can work in real-time, where detection results will be reported within 2–3 s. The IDPS will automatically protect the network by dropping the malicious network packets or block the network ports that are abused by the attackers. In addition, the proposed IDPS can detect network attacks at different locations inside the network by using several client machines to capture data packets and then send information to the server in order to classify types of network attacks. The proposed IDPS also allows system administrator to update existing detection rule sets or learn new training datasets with a friendly graphic user interface. In our experiments, we can correctly detect and prevent network attacks with high accuracy, more than 97 %.

Keywords

Web-based IDPS Real-time detection Intrusion detection system Network security system Machine learning technique 

Notes

Acknowledgements

This work was supported by King Mongkut’s University ofTechnology Thonburi, National Research University Project of Thailand and Office ofthe Higher Education Commission. The authors would like to thank the following members of the network security and optimization group at CPE, KMUTT; P. Jongsuebsuk, E. Wonghirunsombat, T. Assawaniwed and V. Hanchana for their assistance in software programming and running some experiments.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Naruemon Wattanapongsakorn
    • 1
    Email author
  • Chalermpol Charnsripinyo
    • 2
  1. 1.Department of Computer EngineeringKing Mongkut’s University of Technology ThonburiBangkokThailand
  2. 2.National Electronics and Computer Technology CenterPathumthaniThailand

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