Misuse and Anomaly-based Network Intrusion Detection System using Fuzzy and Genetic Classification Algorithms
Abstract
Intrusion Detection System ( IDS) is a topic that has
recently secured much interest in the computer security community. The main function of IDS is distinguishing and predicting normal or abnormal behaviors. The problem of intrusion detection has been studied and received a lot of attention in machine learning and data
mining in the literature survey. The existing techniques are not effective to improve the classification accuracy and to reduce high false alarm rate. Therefore, it is necessary to propose new technique for IDS. In this paper, we propose a new Fuzzy C-Means clustering method and Genetic Algorithm for identifying intrusion and classification for both anomaly and misuse. The experiments of the proposed IDS are performed with KDD cup’99 data set. The
experiments clearly show that the proposed method provides better classification accuracy over existing method.
Keywords
Full Text:
PDFReferences
Chih-Cheng Hung, Sameer Kulkarni, Bor-Chen kuo, “ A New Weighted
Fuzzy C- Means Clustering Algorithm for remotely Sensed image
Classification”, IEEE Journal of Selected Processing, Vol.5,No.3,
pp.543-553,2011.
R. Shanmugavadivu . N.Nagarajan ,” Network Intrusion Detection
System using Fuzzy logic “,Indian journal of computer science and
Engineering ( IJCSE) , Vol.2,No.1,pp.101-111,2011.
Xiaowei Yang, Guangquan Zhang, Jie Lu, Jun Ma, “ A Kernal Fuzzy CMeans
Clustering- Based Fuzzy Support vector Machine Algorithm for
Classification Problems With outliers or Noises”, IEEE transaction on
Fuzzy Systems,Vol.19,No.1,pp.105-114,2011.
Chengjie GU, Shunyi ZHANG, Kai LIU, He Huang, “ Fuzzy Kernel KMeans
Clustering Method based on Immune Genetic Algorithm”,journal
of Computational information Systems,Vol.7,No.1,pp.221-231,2011.
Hossan J,Rahman A,Sayeed S,Samsuddin K, Rokhanni F,” A Modified
Hybrid Fuzzy Clustering Algorithm for Data Partitions”, Australian
Journal Of Basic and Applied Sciences, Vol.5,No.8,pp.674-681,2011.
Yanfei Zhong, Liangpei Zhang, “A New Fuzzy Clustering Algorithm
Based on Clonal Selection for land Cover Clssification”, Mathematical
Problems in Engineering ,2011.
LI Jian-guo, Gao Jing-Wei, “research on Improved Weighted Fuzzy
Clustering Algorithm based on Rough Set”,Proceedings of international
Conference on Computer Engineering and Technology,pp.98-102,2009.
Xuanli L.X and Gerardo B,” A validity measure for Fuzzy Clustering “,
IEEE Transaction pattern Analysis mach.Intell.,Vol.13,No.8,pp.841-
,1991.
Balasko b, Aboyi J, Feil B,” Fuzzy Clustering and Data Analysis
Toolbox for Use with Matlab”,[Online] Available
http://www.fmt.vein.hu/softcomp/. Zadeh L A,”Fuzzy sets”, Information
Control,Vol.8,pp.338-53,1965.
Zadeh L A,”Fuzzy sets”,Information Control,Vol.8,pp.338-53,1965.
Chen W.J, Giger M.L, Bick U,”A fuzzy C-Means(FCM)-based
Approach for Computerized Segmentation of Breast Lesions in
Dynamic Contrast Enhanced MRI Images”,Academic
Radial,Vol.3,No.1,pp.63-72,2006.
jang J.S, Sun C.T, Mizutani,”Neuro-Fuzzy and Soft Computing- A
Computational Approach to Learning and Machine Intelligence”,
Prentice Hall,1997.
Orinella Cominetti, Anastasios Matzavinos, Sandhya Samarasinghe,Don
Kulasiri,Sijia Liu,Philip K. Maini,Radek Erban,” DifFUZZY: a fuzzy
clustering algorithm for complex datasets”, International Journal of
Computational Intelligence in Bioinformatics and Systems
Biology,Vol.1,No.4,2010.
Saghamitra Bandyyopadhyay,”Genetic algorithms for clustering and
fuzzy clustering”,Vol.1,No.6,pp.524-531,2011.
Wang X.”On the Gradient Inverse Weighted Filter”, IEEE Transaction
on Signal Processing,Vol.40,No.2,pp.482-484,1982.
dai Youngshhouna, Li Yuanyuan, Wei Lei,Wang Junling, Zheng
Deling,”Adaptive Immune Genetic Algorithm for Global Optimization
to Multivariable Function”,Journal of Systems Engineering and
Electronics, Vol.18,No.3,pp.655-660,2007.
KDD Cup 1999 Data, Information and Computer Science, University of
California,
Irvine.http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.