Intrusion detection method based on wavelet neural network

Proceedings - 2009 2nd International Workshop on Knowledge Discovery and Data Mining, WKKD 2009

Volumn , Issue , 2009, Pages 851-854

  Sun, Jianjing ^a,b   Yang, Han ^b   Tian, Jingwen ^a,b   Wu, Fan ^a  

^a BEIJING UNION UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

Author keywords

Intrusion behaviors; Intrusion detection; Network security; Wavelet neural network

Indexed keywords

BASIC FUNCTIONS; FAST CONVERGENCE RATE; GRADIENT DESCENT; IMPACT FACTOR; INTRUSION BEHAVIORS; INTRUSION DETECTION METHOD; NONLINEAR FUNCTIONS; SAMPLE DATA; SPARSENESS PROPERTY; WAVELET NETWORK; WAVELET NEURAL NETWORK; WAVELET NEURAL NETWORKS;

EDUCATION; INFORMATION MANAGEMENT; INTERNET; LEARNING ALGORITHMS; MINING; NETWORK SECURITY; NEURAL NETWORKS;

INTRUSION DETECTION;

EID: 66249125727     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WKDD.2009.214     Document Type: Conference Paper

References (10)

1. Wang Guojun, Yue Zhiqiang, "Application Research of Support Vector Machine in the Intrusion Detection", GUANGXI JOURNAL OF LIGHT INDUSTRY, no. 7, pp. 51-52, 2008.
2. Luan Qinglin, Lu Huibin, "Research of intrusion detection based on neural network optimized by adaptive genetic algorithm", Computer Engineering and Design, vol.29,no. 12, pp. 3022-3025, 2008.
3. Jiao Licheng. Neural network system theory. Xian: Xi an electronic science and technology university press, 1995.
4. Pati Y C, Krishnaprasad P S. Analysis and sythesis of feedforward neural networks using discrete affine wavelet transformations. IEEE Trans Neural Networks, vol.4, no. 1, pp. 73-85, 1993.
5. Zhang Q, Benveniste A. Wavelet networks. IEEE Trans Neural Networks, vol.3, no. 6, pp. 889-898, 1992.
6. Juan M E T, Pedro G T, Jesus E D V, et al, "Anomaly detection methods in wired networks", A survey and taxonomy, Computer Communications, vol.27, no.16, pp. 1569-1584, 2004.
7. Theuns V, Ray H, "Intrusion detection techniques and approaches", Computer Communications, vol.25, no. 15, pp. 1356-1584, 2002.
8. Lincoln Laboratory, Massachusetts Institute of Technology. DARPA intrusion detection evaluation [EB/OL]. http://www.11.mit.edu/IST/ideval/index. html, 2003-06-16.
9. JOO D, HON G T, HAN I, "The neural network models for IDS based on the asymmet ric cost s of false negative errors and false positive errors", Expert System with Applications, vol.25, no. 1, pp. 69-75, 2003.
10. Zhao zhenning, Xu yongmao. Introduction to fuzzy theory and neural networks and their application, Beijing: Tsinghua University Press, 1996.