Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
ÇѱÛÁ¦¸ñ(Korean Title) |
¼Ò¼È ³×Æ®¿öÅ© »ó¿¡¼ÀÇ Àç±ÍÀû ³×Æ®¿öÅ© ±¸Á¶ Ư¼ºÀ» È°¿ëÇÑ ½ºÆÔŽÁö ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Social Network Spam Detection using Recursive Structure Features |
ÀúÀÚ(Author) |
À庸¿¬
Á¤½ÃÇö
±èÁ¾±Ç
Boyeon Jang
Sihyun Jeong
Chongkwon Kim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 44 NO. 11 PP. 1231 ~ 1235 (2017. 11) |
Çѱ۳»¿ë (Korean Abstract) |
¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ©´Â Á¤º¸ÀüÆÄÀÇ ¿ëÀ̼º ¹× ÆÄ±Þ ¿µÇâ·ÂÀÌ ³ôÁö¸¸ À̸¦ ¾ÇÀÇÀûÀ¸·Î È°¿ëÇϱâ À§ÇÑ ½ºÆиӵéÀÌ ´Ù¼ö È°µ¿ ÁßÀÌ´Ù. ÀÌ·¯ÇÑ ½ºÆиӸ¦ ½Äº°Çϱâ À§ÇÑ ½ºÆÔ Å½Áö±â¹ý ¿¬±¸°¡ ´Ù¾çÇÑ ºÐ¾ß¿¡¼ ÀÌ·ç¾îÁö°í ÀÖÁö¸¸ ½ºÆÐ¸Óµé ¶ÇÇÑ ½ºÆÔ ³»¿ëÀ̳ª ½ºÆÔ¸µÅ©, È°µ¿ Áֱ⠵îÀÇ Æ¯¼ºÀ» º¯°æÇÏ¿© ŽÁö¸¦ ÇÇÇÏ°í ÀÖ´Ù. ÇÏÁö¸¸ ´Ù¸¥ Ư¼ºµé°ú ´Þ¸® ¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ©ÀÇ °íÀ¯ ³×Æ®¿öÅ© Ư¼ºÀÎ ¸µÅ© Ư¼ºÀº ½±°Ô º¯È½ÃÅ°´Â ¾î·Æ´Ù. µû¶ó¼ º» ³í¹®¿¡¼´Â ÀÌ·¯ÇÑ ³×Æ®¿öÅ©ÀÇ ±¸Á¶ÀûÀΠƯ¼ºÀ» È°¿ëÇÏ¿© ½ºÆиӸ¦ ÀϹݻç¿ëÀÚ¿Í ±¸ºÐÇÏ´Â ¹æ¹ýÀ» Á¦½ÃÇÑ´Ù. Áï ÀϹݻç¿ëÀÚ ³ëµå°¡ ÁÖº¯ ³ëµå¿Í ºñ½ÁÇÑ ³×Æ®¿öÅ© Ư¼ºÀ» °®´Â Á¡¿¡ ÁÖ¸ñÇÏ¿© ÀÎÁ¢ ³ëµå¸¦ È°¿ëÇÑ Àç±ÍÀûÀÎ ±¸Á¶Àû Ư¼ºÀ» »ý¼ºÇÏ¿© È°¿ëÇÔÀ¸·Î½á ½ºÆиÓÀÇ ½Äº°È®·üÀ» ³ôÀÌ°í ÀÖ´Ù. À̸¦ °ËÁõÇϱâ À§ÇÑ ½ÇÇèÀº Æ®À§ÅÍÀÇ ½ÇÁ¦ µ¥ÀÌÅͼÂÀ» Weka ÇÁ·Î±×·¥¿¡ žÀçµÈ ·£´ýÆ÷·¹½ºÆ® ¾Ë°í¸®ÁòÀ» È°¿ëÇÏ¿© ÃøÁ¤ÇÏ¿´À¸¸ç, Àç±ÍÀûÀΠƯ¼ºÀ» È°¿ëÇÏÁö ¾Ê´Â ¹æ¹ý°ú ±âÁ¸ Á¦¾È ¾Ë°í¸®Áò¿¡ ºñÇØ Å½ÁöÀ²ÀÌ 0.82¿¡¼ 0.90À¸·Î Çâ»óµÊÀ¸·Î½á Á¦¾ÈÇÏ´Â ¹æ¹ýÀÌ ½ºÆиӸ¦ ŽÁöÇϴµ¥ È¿°úÀûÀÓÀ» Á¦½ÃÇÏ°í ÀÖ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Given the network structure in online social network, it is important to determine a way to distinguish spam accounts from the network features. In online social network, the service provider attempts to detect social spamming to maintain their service quality. However the spammer group changes their strategies to avoid being detected. Even though the spammer attempts to act as legitimate users, certain distinguishable structural features are not easily changed. In this paper, we investigate a way to generate meaningful network structure features, and suggest spammer detection method using recursive structural features. From a result of real-world dataset experiment, we found that the proposed algorithm could improve the classification performance by about 8%.
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Å°¿öµå(Keyword) |
¿Â¶óÀÎ ¼Ò¼È ³×Æ®¿öÅ©
½ºÆÔ
½ºÆиÓ
½ºÆÔŽÁö ±â¹ý
Àç±ÍÀû ³×Æ®¿öÅ© ±¸Á¶ Ư¼º
Àç±ÍÀû Ư¼º
online social networks
spam
spammer
spam detection
recursive structure features
feature extraction
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ÆÄÀÏ÷ºÎ |
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