PeANFIS-FARM Framework in Defending against Web Service Attacks

DOI

10.2991/ccis-13.2013.26How to use a DOI?

Keywords

intrusion detection; intrusion prevention; fuzzy association rule mining; e-commerce; web services

Abstract

Internet-enabled Web Service (WS) applications, such as e-commerce, are facing eXtensible Markup Language (XML)-related security threats. However, network and host-based intrusion (ID) and prevention (IP) systems and Web Service Security (WSS) standards are inadequate in countering against these threats. This paper presents a framework to mitigate XML/SOAP attacks. Our framework comprises of two intelligent models: the policy-enhanced adaptive neuro-fuzzy inference system (PeANFIS) and fuzzy association rule mining (FARM) model. Performance evaluation of each model indicates detection rate of greater than 99% and false alarm rate of less than 1%. In this paper, we aim to help the security administrator to decide which model to implement depending on the context of the situation. We present rule-based cases as examples to guide design and implementation decisions. Our future work shall see the implementation of the PeANFIS-FARM framework on a wider scale and in cloud computing.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chan Gaik-Yee
AU  - Lee Chien-Sing
AU  - Heng Swee-Huay
PY  - 2013/11
DA  - 2013/11
TI  - PeANFIS-FARM Framework in Defending against Web Service Attacks
BT  - Proceedings of the The 1st International Workshop on Cloud Computing and Information Security
PB  - Atlantis Press
SP  - 108
EP  - 112
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccis-13.2013.26
DO  - 10.2991/ccis-13.2013.26
ID  - Gaik-Yee2013/11
ER  -