2014³â Ãß°èÇмú´ëȸ
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
³×Æ®¿öÅ© ħÀÔ Å½Áö¸¦ À§ÇÑ ÃÖÀû Ư¡ ¼±Åà ¾Ë°í¸®Áò |
¿µ¹®Á¦¸ñ(English Title) |
An optimal feature selection algorithm for the network intrusion detection system |
ÀúÀÚ(Author) |
Á¤½ÂÇö
¹®ÁØ°É
°½ÂÈ£
Seung-Hyun Jung
Jun-Geol Moon
Seung-Ho Kang
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 18 NO. 02 PP. 0342 ~ 0345 (2014. 10) |
Çѱ۳»¿ë (Korean Abstract) |
±â°èÇнÀÀ» ÀÌ¿ëÇÑ ³×Æ®¿öÅ© ħÀÔŽÁö½Ã½ºÅÛÀº ¼±ÅÃµÈ Æ¯Â¡ Á¶ÇÕ¿¡ µû¶ó Á¤È®¼º ¹× È¿À²¼º Ãø¸é¿¡¼ Å©°Ô ¿µÇâÀ» ¹Þ´Â´Ù. ÇÏÁö¸¸ ÀϹÝÀûÀ¸·Î »ç¿ëµÇ´Â ħÀÔŽÁö¿ë Ư¡µé·ÎºÎÅÍ ÃÖÀûÀÇ Á¶ÇÕÀ» ã¾Æ³»´Â ÀÏÀº ¸¹Àº °è»ê·®À» ¿ä±¸ÇÑ´Ù. ¿¹¸¦ µé¾î n°³·Î ±¸¼ºµÈ Ư¡µé·ÎºÎÅÍ °¡´ÉÇÑ Æ¯Â¡Á¶ÇÕÀº 2n-1 °³ÀÌ´Ù. º» ³í¹®¿¡¼´Â ÀÌ·¯ÇÑ ¹®Á¦¸¦ ÇØ°áÇϱâ À§ÇÑ ÃÖÀû Ư¡ ¼±Åà ¾Ë°í¸®ÁòÀ» Á¦½ÃÇÑ´Ù. Á¦¾ÈÇÑ ¾Ë°í¸®ÁòÀº ÃÖÀûÈ ¹®Á¦ ÇØ°áÀ» À§ÇÑ ´ëÇ¥ÀûÀÎ ¸ÞŸ ÈÞ¸®½ºÆ½ ¾Ë°í¸®ÁòÀÎ Áö¿ªÅ½»ö ¾Ë°í¸®Áò¿¡ ±â¹Ý ÇÑ´Ù. ¶ÇÇÑ Æ¯Â¡ Á¶ÇÕÀ» Æò°¡¸¦ À§ÇØ ¼±ÅÃµÈ Æ¯Â¡ ¿ä¼Ò¿Í k-means ±ºÁýÈ ¾Ë°í¸®ÁòÀ» ÀÌ¿ëÇØ ±¸ÇØÁø ±ºÁýÈÀÇ Á¤È®¼ºÀ» ºñ¿ëÇÔ¼ö·Î »ç¿ëÇÑ´Ù. Á¦¾ÈÇÑ Æ¯Â¡ ¼±Åà ¾Ë°í¸®ÁòÀÇ Æò°¡¸¦ À§ÇØ NSL-KDD µ¥ÀÌÅÍ¿Í Àΰø ½Å°æ¸ÁÀ» »ç¿ëÇØ Æ¯Â¡ ¸ðµÎ¸¦ »ç¿ëÇÑ °æ¿ì¿Í ºñ±³ÇÑ´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Network intrusion detection system based on machine learning methods is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features from generally used features to detect network intrusion requires extensive computing resources. For instance, the number of possible feature combinations from given n features is 2n-1. In this paper, to tackle this problem we propose a optimal feature selection algorithm. Proposed algorithm is based on the local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In addition, the accuracy of clusters which obtained using selected feature components and k-means clustering algorithm is adopted to evaluate a feature assembly. In order to estimate the performance of our proposed algorithm, comparing with a method where all features are used on NSL-KDD data set and multi-layer perceptron. |
Å°¿öµå(Keyword) |
network intrusion detection system
machine learning
feature selection
local search algorithm
|
ÆÄÀÏ÷ºÎ |
PDF ´Ù¿î·Îµå
|