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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Current Result Document : 7 / 10 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) ¹®ÀåÀ¯»çµµ ÃøÁ¤ ±â¹ýÀ» ÅëÇÑ ½ºÆÔ ÇÊÅ͸µ ½Ã½ºÅÛ ±¸Çö
¿µ¹®Á¦¸ñ(English Title) Implementation of a Spam Message Filtering System using Sentence Similarity Measurements
ÀúÀÚ(Author) ¿ì¼öºó   ÀÌÁ¾¿ì   SooBin Ou   Jongwoo Lee  
¿ø¹®¼ö·Ïó(Citation) VOL 23 NO. 01 PP. 0057 ~ 0064 (2017. 01)
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(Korean Abstract)
¹®ÀÚ ¸Þ½ÃÁö´Â ÈÞ´ëÆùÀ» »ç¿ëÇÏ´Â »ç¶÷µé¿¡°Ô Áß¿äÇÑ ÀÇ»ç¼ÒÅëÀÇ ¹æ¹ý Áß ÇϳªÀÌ´Ù. ¶ÇÇÑ Ä£±¸¸Î±â ¹æ½ÄÀÌ ÇÊ¿ä¾øÀÌ »ç¿ëÀÌ °¡´ÉÇϱ⠶§¹®¿¡ À̸¦ ¾Ç¿ëÇÑ ºÒ¹ý ±¤°í ½ºÆÔ¸Þ½ÃÁö°¡ ±â½ÂÀ» ºÎ¸®°í ÀÖ´Ù. ÃÖ±Ù ½ºÆÔ ÇÊÅ͸µÀ» À§ÇØ ±â°è ÇнÀÀ» ÀÌ¿ëÇÑ ½Ã½ºÅÛµéÀÌ µîÀå ÇÏ¿´Áö¸¸ ¸¹Àº °è»êÀ» ÇÊ¿ä·Î ÇÏ´Â ´ÜÁ¡ÀÌ ÀÖ´Ù. º» ³í¹®¿¡¼­´Â °Ë»öÇÒ Äõ¸®¸¦ ÀÔ·ÂÇÒ ¶§ ºÎÁ¤È®ÇÑ Äõ¸®¸¦ ÀÔ·ÂÇÏ´õ¶óµµ ÀúÀåµÈ µ¥ÀÌÅͺ£À̽º¿Í ºñ±³ÇÏ¿© °¡Àå ºñ½ÁÇÑ ´Ü¾î¸¦ Â÷¼ö °³³äÀ» Àû¿ëÇÏ¿© À¯ÃßÇÏ´Â ÁýÇÕ±â¹Ý POI(Point of Interest) °Ë»ö ¾Ë°í¸®ÁòÀ» ÀÌ¿ëÇÏ¿© ½ºÆÔ ÇÊÅ͸µ ½Ã½ºÅÛÀ» ±¸ÇöÇÏ¿´´Ù. ÀÌ ¾Ë°í¸®ÁòÀ» Àû¿ëÇÏ¸é ¼­¹ö ÄÄÇ»Æà ¾øÀÌ ¹®ÀÚÀÇ Á¶ÇÕ¸¸À» ÀÌ¿ëÇØ Äõ¸®¸¦ À¯ÃßÇÒ ¼ö Àֱ⠶§¹®¿¡ ½ºÆÔ ÇÊÅ͸µ¿¡ Àû¿ëÇÏ¿© ÀÔ·ÂµÈ ¹®ÀÚ¸Þ½ÃÁö°¡ ±³¹¦ÇÏ°Ô º¯ÇüµÇ´õ¶óµµ ½ºÆÔÀ̶ó°í ÇÊÅ͸µÀÌ °¡´ÉÇÏ´Ù. ¶ÇÇÑ ¹®Àå À¯»çµµ ÃøÁ¤ ±â¹ýÀ» È°¿ëÇÏ¿© ½ºÆÔ ÇÊÅ͸µ ¼º´ÉÀ» Çâ»ó½ÃÄ×À¸¸ç, ½ºÆÔ ÇÊÅ͸µ¿¡ Ãë¾àÇÑ Æ¯Á¤ À¯Çüµµ °É·¯³»±â À§ÇØ Æ¯Á¤ Àü󸮰úÁ¤À» Áö¿øÇÔÀ¸·Î½á ´ëºÎºÐÀÇ ½ºÆÔ ¸Þ½ÃÁö¸¦ ÇÊÅ͸µ °¡´ÉÇϵµ·Ï ÇÏ¿´´Ù. ±âÁ¸ ÁýÇÕ±â¹Ý POI °Ë»ö ¾Ë°í¸®Áò°ú À̸¦ È®Àå½ÃŲ ¹®Àå À¯»çµµ ÃøÁ¤ ±â¹ý, ƯÁ¤ Àüó¸® °úÁ¤À» Ãß°¡ÇÑ ½Ã½ºÅÛÀ¸·Î ÇÊÅ͸µ ½Ã½ºÅÛÀÇ ¼º´É Æò°¡¸¦ ÁøÇàÇÏ¿´´Ù. ±× °á°ú º» ³í¹®¿¡¼­ ±¸ÇöÇÑ ½Ã½ºÅÛÀÌ ±âÁ¸ ÁýÇÕ ±â¹Ý POI ¾Ë°í¸®Áò°ú ºñ±³ÇÏ¿© Çâ»óµÈ ½ºÆÔ ÇÊÅ͸µ ¼º´ÉÀ» º¸¿©ÁÖ´Â °ÍÀ» È®ÀÎ ÇÏ¿´´Ù. ¶ÇÇÑ À̵¿ Åë½Å»ç 3»ç¿¡¼­ ÇÊÅ͸µ¿¡ Ãë¾àÇÑ À¯ÇüÀÌ º» ³í¹®¿¡¼­ ±¸ÇöÇÑ ½Ã½ºÅÛÀ¸·Î ³ôÀº ¼º´ÉÀ¸·Î ÇÊÅ͸µÀÌ °¡´ÉÇÏ´Ù´Â °ÍÀ» È®ÀÎÇÏ¿´´Ù.
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(English Abstract)
Short message service (SMS) is one of the most important communication methods for people who use mobile phones. However, illegal advertising spam messages exploit people because they can be used without the need for friend registration. Recently, spam message filtering systems that use machine learning have been developed, but they have some disadvantages such as requiring many calculations. In this paper, we implemented a spam message filtering system using the set-based POI search algorithm and sentence similarity without servers. This algorithm can judge whether the input query is a spam message or not using only letter composition without any server computing. Therefore, we can filter the spam message although the input text message has been intentionally modified. We added a specific preprocessing option which aims to enable spam filtering. Based on the experimental results, we observe that our spam message filtering system shows better performance than the original set-based POI search algorithm. We evaluate the proposed system through extensive simulation. According to the simulation results, the proposed system can filter the text message and show high accuracy performance against the text message which cannot be filtered by the 3 major telecom companies.
Å°¿öµå(Keyword) ½ºÆÔ¸Þ½ÃÁö   ÇÊÅ͸µ   ¹®ÀåÀ¯»çµµÃøÁ¤±â¹ý   ÁýÇÕ±â¹ÝPOI °Ë»ö¾Ë°í¸®Áò   spam message   filtering   sentence similarity measurement method   set-based POI search algorithm  
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