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Algorithm of Network Intrusion Detection Based on AdaBoost and PNN(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2008年04期
Page:
21-24
Research Field:
Publishing date:

Info

Title:
Algorithm of Network Intrusion Detection Based on AdaBoost and PNN
Author(s):
Chen Chunling; Shang Zihao
College of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
Keywords:
intrusion de tection;  probab ilistic neura l netw ork( PNN);  AdaBoost;  ABPNN
PACS:
TP393.08
DOI:
-
Abstract:
By com bining AdaBoost a lgo rithm w ith probabilistic neural netwo rk ( PNN) , a new probab ilistic neura l ne-t w ork ( ABPNN) m ode l is proposed. Based on th is mode,l a new in trusion detection a lgo rithm is suggested. This algorithm ana ly zes and estim ates the network data rece ived, rea lizes the acctom atic sorting of intrusion m ethods, and at the sam e tim e sorts and m em orizes new types o f intrusion m ethods. Exper iments show that the propo sed algor ithm can g et better perform ance in detection rate and a larm rate.

References:

[ 1] Va liant L G. A theory of learnable[ J]. Commun ica tion o f ACM, 1984, 27( 11): 1 134-1 142.
[ 2] Schapire R E. The strength of w eak learnab ility [ J]. M achine Learn ing, 1990, 5( 2) : 197-227.
[ 3] Zaknich A. In troduction to themod ified probab ilistic neural netwo rk for gene ra l s igna l pro cessing app lications[ J]. IEEE Transactions on S igna l Processing, 1998, 46( 7): 1 980-1 990.
[ 4] Freund Y, Schap ire R. Expe rim en ts w ith a new boosting algor ithm [ C ] / / Sa itta L. Proc o f the 13th Int. 1 Conf onM achine Learning. Austin: M organ Kanfm ann, 1996: 148-156.
[ 5] Liu B, H suW, M a Y. Prun ing and summ ar izing the d iscove red assoc ia tions[ Z] . KDD- 99, 1999.
[ 6] 刘道群. 基于遗传神经网络的入侵检测模型的研究[ D]. 重庆: 重庆大学, 2005.
Liu Daoqun. S tudy o f IDS model based on genetic neura l network[ D]. Chongq ing: Chongqing Un iversity, 2005. ( in Chinese)
[ 7] Jackson D A, Yong C. Robust pr inc ipal component analysis and outlier detection w ith eco log ical data[ J]. Env ironm etr ics,2004, 15( 2): 129-139.

Memo

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Last Update: 2013-04-24