Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
ÇѱÛÁ¦¸ñ(Korean Title) |
N-±×·¥ Áõ° ³ªÀÌºê º£À̽º ¾Ë°í¸®Áò°ú ÀϹÝÈµÈ k-Àý´Ü ¼ÇȽº Æ®¸®¸¦ ÀÌ¿ëÇÑ È®Àå °¡´ÉÇÏ°í Á¤È®ÇÑ Ä§ÀÔ Å½Áö ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Scalable and Accurate Intrusion Detection using n-Gram Augmented Naive Bayes and Generalized k-Truncated Suffix Tree |
ÀúÀÚ(Author) |
°´ë±â
Ȳ±âÇö
Dae-Ki Kang
Gi-Hyun Hwang
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¿ø¹®¼ö·Ïó(Citation) |
VOL 13 NO. 04 PP. 0805 ~ 0812 (2009. 04) |
Çѱ۳»¿ë (Korean Abstract) |
±â°è ÇнÀÀ» ÀÀ¿ëÇÑ ¸¹Àº ħÀÔ Å½Áö ½Ã½ºÅ۵鿡¼ n-±×·¥ Á¢±Ù ¹æ¹ýÀÌ »ç¿ëµÇ°í ÀÖ´Ù. ±×·¯³ª, n-±×·¥ Á¢±Ù ¹æ¹ýÀº È®ÀåÀÌ ¾î·Æ°í, ÁÖ¾îÁø ½ÃÄö½º¿¡¼ ȹµæÇÑ n-±×·¥µéÀÌ ¼·Î °ãÄ¡´Â ¹®Á¦µéÀ» °¡Áö°í ÀÖ´Ù. º» ¿¬±¸¿¡¼´Â ÀÌ·¯ÇÑ ¹®Á¦µéÀ» ÇØ°áÇϱâ À§ÇØ, ÀϹÝÈµÈ k-Àý´Ü ¼ÇȽº Æ®¸® (generalized k-truncated suffix tree; k-TST) ±â¹ÝÀÇ n-±×·¥ Áõ° ³ªÀÌºê º£À̽º (n-gram augmented naive Bayes) ¾Ë°í¸®ÁòÀ» ħÀÔ ½ÃÄö½ºÀÇ ºÐ·ù¿¡ Àû¿ëÇÏ¿© º¸¾Ò´Ù. Á¦¾ÈµÈ ½Ã½ºÅÛÀÇ ¼º´ÉÀ» Æò°¡Çϱâ À§ÇØ n-±×·¥ Ư¡µéÀ» »ç¿ëÇÏ´Â ÀÏ¹Ý ³ªÀÌºê º£À̽º (naive Bayes) ¾Ë°í¸®Áò°ú ¼Æ÷Æ® º¤ÅÍ ¸Ó½Å (support vector machines) ¾Ë°í¸®Áò°ú º» ¿¬±¸¿¡¼ Á¦¾ÈÇÑ n-±×·¥ Áõ° ³ªÀÌºê º£À̽º ¾Ë°í¸®ÁòÀ» È£½ºÆ® ±â¹Ý ħÀÔ Å½Áö º¥Ä¡¸¶Å© µ¥ÀÌÅÍ¿Í ºñ±³ÇÏ¿´´Ù. °ø°³µÈ È£½ºÆ® ±â¹Ý ħÀÔ Å½Áö º¥Ä¡¸¶Å© µ¥ÀÌÅÍÀÎ ´º ¸ß½ÃÄÚ ´ëÇÐ(University of New Mexico)ÀÇ º¥Ä¡¸¶Å© µ¥ÀÌÅÍ¿¡ Àû¿ëÇØ º» °á°ú¿¡ µû¸£¸é, n-±×·¥ Áõ° ¹æ¹ýÀÌ, n-±×·¥ÀÌ ³ªÀÌºê º£À̽º¿¡ Á÷Á¢ Àû¿ëµÇ´Â °æ¿ì(¿¹: n-±×·¥ Ư¡À» »ç¿ëÇÏ´Â ÀÏ¹Ý ³ªÀÌºê º£À̽º), »ý±â´Â µ¶¸³¼º °¡Á¤¿¡ ´ëÇÑ À§¹èÀÇ ¹®Á¦µµ ÇØ°áÇϸé¼, µ¿½Ã¿¡ ´õ Á¤È®ÇÑ Ä§ÀÔ Å½Áö±â¸¦ »ý¼ºÇØ ³¿À» ¾Ë ¼ö ÀÖ¾ú´Ù.
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¿µ¹®³»¿ë (English Abstract) |
In many intrusion detection applications, n-gram approach has been widely applied. However, n-gram approach has shown a few problems including unscalability and double counting of features. To address those problems, we applied n-gram augmented Naive Bayes with k-truncated suffix tree (k-TST) storage mechanism directly to classify intrusive sequences and compared performance with those of Naive Bayes and Support Vector Machines (SVM) with n-gram features by the experiments on host-based intrusion detection benchmark data sets. Experimental results on the University of New Mexico (UNM) benchmark data sets show that the n-gram augmented method, which solves the problem of independence violation that happens when n-gram features are directly applied to Naive Bayes (i.e. Naive Bayes with n-gram features), yields intrusion detectors with higher accuracy than those from Naive Bayes with n-gram features and shows comparable accuracy to those from SVM with n-gram features. For the scalable and efficient counting of n-gram features, we use k-truncated suffix tree mechanism for storing n-gram features. With the k-truncated suffix tree storage mechanism, we tested the performance of the classifiers up to 20-gram, which illustrates the scalability and accuracy of n-gram augmented Naive Bayes with k-truncated suffix tree storage mechanism.
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Å°¿öµå(Keyword) |
N-±×·¥ ³ªÀÌºê º£À̽º ¾Ë°í¸®Áò
ÀϹÝÈµÈ k-Àý´Ü ¼ÇȽº Æ®¸®
È£½ºÆ® ±â¹Ý ħÀÔ Å½Áö
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