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Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
ÀÌ»ó ŽÁö ºÐ¼®¿¡¼ ¾Ë·ÁÁöÁö ¾Ê´Â °ø°ÝÀ» ½Äº°Çϱâ À§ÇÑ ÀÌ»ê ¿þÀÌºí¸´ º¯È¯ Àû¿ë ¿¬±¸ |
¿µ¹®Á¦¸ñ(English Title) |
Application of Discrete Wavelet Transforms to Identify Unknown Attacks in Anomaly Detection Analysis |
ÀúÀÚ(Author) |
ÀåÀ¯Áø
±èÁö¿µ
ÀÌÁÖÇö
Ȳ ÁØ
Yoo-jin Jang
Ji-yeong Kim
Ju-hyun Lee
Jun Hwang
±èµ¿¿í
½Å°ÇÀ±
À±Áö¿µ
±è»ó¼ö
ÇÑ¸í¹¬
Dong-Wook Kim
Gun-Yoon Shin
Ji-Young Yun
Sang-Soo Kim
Myung-Mook Han
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¿ø¹®¼ö·Ïó(Citation) |
VOL 22 NO. 03 PP. 0045 ~ 0052 (2021. 06) |
Çѱ۳»¿ë (Korean Abstract) |
»çÀ̹ö º¸¾ÈÀÇ Ä§ÀÔŽÁö ½Ã½ºÅÛ¿¡¼ ¾Ë·ÁÁöÁö ¾Ê´Â °ø°ÝÀ» ½Äº°Çϱâ À§ÇÑ ¸¹Àº ¿¬±¸°¡ ÀÌ·ç¾îÁö°í ÀÖÁö¸¸, ±× Áß¿¡¼µµ ÀÌ»óÄ¡¸¦ ±â¹ÝÀ¸·Î ÇÏ´Â ¿¬±¸°¡ ÁÖ¸ñ¹Þ°í ÀÖ´Ù. ÀÌ¿¡ µû¶ó ¿ì¸®´Â ¾Ë·ÁÁöÁö ¾Ê´Â °ø°Ý¿¡ ´ëÇÑ ¹üÁÖ¸¦ Á¤ÀÇÇÏ¿© ÀÌ»óÄ¡¸¦ ½Äº°ÇÑ´Ù. ¾Ë·ÁÁöÁö ¾Ê´Â °ø°ÝÀº 2°¡Áö ¹üÁÖ·Î Á¶»çÇÏ¿´´Âµ¥, ù°´Â º¯Á¾ °ø°ÝÀ» »ý¼ºÇÏ´Â »çÇ×ÀÌ ÀÖ°í, µÎ ¹ø°´Â »õ·Î¿î À¯ÇüÀ¸·Î ºÐ·ùÇÏ´Â ¿¬±¸·Î ³ª´©¾ú´Ù. ¿ì¸®´Â º¯Á¾ °ø°ÝÀ» »ý¼ºÇÏ´Â ¿¬±¸ ¹üÁÖ¿¡¼ º¯Á¾°ú °°ÀÌ À¯»ç µ¥ÀÌÅ͸¦ ½Äº°ÇÒ ¼ö ÀÖ´Â ÀÌ»óÄ¡ ¿¬±¸¸¦ ¼öÇàÇÏ¿´´Ù. ħÀÔŽÁö ½Ã½ºÅÛ¿¡¼ ÀÌ»óÄ¡¸¦ ½Äº°ÇÏ´Â Å« ¹®Á¦´Â Á¤»óÇൿ°ú °ø°ÝÇൿÀÌ °°Àº °ø°£À» °øÀ¯ÇÏ´Â °ÍÀÌ´Ù. À̸¦ À§ÇØ ¿ì¸®´Â ÀÌ»ê ¿þÀÌºí¸´ º¯È¯À¸·Î Á¤»ó°ú °ø°Ý¿¡ ´ëÇØ ¸íÈ®ÇÑ À¯ÇüÀ¸·Î ³ª´ ¼ö ÀÖ´Â ±â¹ýÀ» Àû¿ëÇÏ°í ÀÌ»óÄ¡¸¦ ŽÁöÇÏ¿´´Ù. °á°ú·Î ¿ì¸®´Â ÀÌ»ê ¿þÀÌºí¸´ º¯È¯À¸·Î À籸¼ºµÈ µ¥ÀÌÅÍ¿¡¼ One-Class SVMÀ» ÅëÇÑ ÀÌ»óÄ¡¸¦ ½Äº° ÇÒ ¼ö ÀÖÀ½À» È®ÀÎÇÏ¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Although many studies have been conducted to identify unknown attacks in cyber security intrusion detection systems, studies based on outliers are attracting attention. Accordingly, we identify outliers by defining categories for unknown attacks. The unknown attacks were investigated in two categories: first, there are factors that generate variant attacks, and second, studies that classify them into new types. We have conducted outlier studies that can identify similar data, such as variants, in the category of studies that generate variant attacks. The big problem of identifying anomalies in the intrusion detection system is that normal and aggressive behavior share the same space. For this, we applied a technique that can be divided into clear types for normal and attack by discrete wavelet transformation and detected anomalies. As a result, we confirmed that the outliers can be identified through One-Class SVM in the data reconstructed by discrete wavelet transform. |
Å°¿öµå(Keyword) |
iOS
Semantic Segmentation
±íÀÌ
¿µ»óó¸®
µà¾óÄ«¸Þ¶ó
¸ð¹ÙÀÏ ¾ÖÇø®ÄÉÀ̼Ç
iOS
Semantic Segmentation
Depth
Computer Vision
Dual Camera
Mobile Application
¾Ë·ÁÁöÁö ¾Ê´Â °ø°Ý
ÀÌ»ê ¿þÀÌºí¸´ º¯È¯
ÀÌ»ó ŽÁö
One-Class SVM
Unknown Attack
discrete wavelet transform
Anomaly Detection
One-Class SVM
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