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Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
Performance Counter Monitor¸¦ ÀÌ¿ëÇÑ FLUSH+RELOAD °ø°Ý ½Ç½Ã°£ ŽÁö ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
Real-Time Detection on FLUSH+RELOAD Attack Using Performance Counter Monitor |
ÀúÀÚ(Author) |
Á¶Á¾Çö
±èÅÂÇö
½Å¿µÁÖ
Jonghyeon Cho
Taehyun Kim
Youngjoo Shin
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¿ø¹®¼ö·Ïó(Citation) |
VOL 08 NO. 06 PP. 0151 ~ 0158 (2019. 06) |
Çѱ۳»¿ë (Korean Abstract) |
ij½Ã ºÎä³Î °ø°Ý Áß ÇϳªÀÎ FLUSH+RELOAD °ø°ÝÀº ³ôÀº ÇØ»óµµ¿Í ÀûÀº ³ëÀÌÁî·Î ¿©·¯ ¾Ç¼º ÇÁ·Î±×·¥¿¡¼µµ È°¿ëµÇ´Â µî ºñ¹Ð Á¤º¸ÀÇ À¯Ãâ¿¡ ´ëÇÑ À§Ç輺ÀÌ ³ôÀº °ø°ÝÀÌ´Ù. µû¶ó¼ ÀÌ °ø°ÝÀ» ¸·±â À§ÇØ ½Ç½Ã°£À¸·Î °ø°ÝÀ» ŽÁöÇÏ´Â ±â¼úÀ» °³¹ßÇÒ ÇÊ¿ä°¡ ÀÖ´Ù. º» ³í¹®¿¡¼´Â ÇÁ·Î¼¼¼ÀÇ PCM (Performance Counter Monitor)¸¦ ÀÌ¿ëÇÑ ½Ç½Ã°£ FLUSH+RELOAD °ø°Ý ŽÁö ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. ŽÁö ¹æ¹ýÀÇ °³¹ßÀ» À§ÇØ ¿ì¼± °ø°ÝÀÌ ¹ß»ýÇÏ´Â µ¿¾È PCMÀÇ ¿©·¯ Ä«¿îÅ͵éÀÇ °ªµéÀÇ º¯È¸¦ 4°¡Áö ½ÇÇèÀ» ÅëÇØ °üÂûÇÏ¿´´Ù. ±× °á°ú, 3°¡Áö Áß¿äÇÑ ¿äÀο¡ ÀÇÇØ °ø°Ý ŽÁö¸¦ ÇÒ ¼ö ÀÖ´Ù´Â °ÍÀ» ¹ß°ßÇÏ¿´´Ù. À̸¦ ¹ÙÅÁÀ¸·Î ¸Ó½Å ·¯´×ÀÇ logistic regression°ú ANN(Artificial Neural Network)¸¦ »ç¿ëÇØ °á°ú¿¡ ´ëÇÑ °¢°¢ ÇнÀÀ» ½ÃŲ µÚ ½Ç½Ã°£À¸·Î °ø°Ý¿¡ ´ëÇÑ Å½Áö¸¦ ÇÒ ¼ö ÀÖ´Â ¾Ë°í¸®ÁòÀ» °³¹ßÇÏ¿´´Ù. ÀÌ Å½Áö ¾Ë°í¸®ÁòÀº ÀÏÁ¤ÇÑ ½Ã°£µ¿¾È °ø°ÝÀ» ÁøÇàÇÏ¿© ¸ðµç °ø°ÝÀ» °¨ÁöÇϴµ¥ ¼º°øÇÏ¿´°í »ó´ëÀûÀ¸·Î ÀûÀº ¿ÀŽ·üÀ» º¸¿©ÁÖ¾ú´Ù.
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¿µ¹®³»¿ë (English Abstract) |
FLUSH RELOAD attack exposes the most serious security threat among cache side channel attacks due to its high resolution and low noise. This attack is exploited by a variety of malicious programs that attempt to leak sensitive information. In order to prevent such information leakage, it is necessary to detect FLUSH RELOAD attack in real time. In this paper, we propose a novel run-time detection technique for FLUSH RELOAD attack by utilizing PCM (Performance Counter Monitor) of processors. For this, we conducted four kinds of experiments to observe the variation of each counter value of PCM during the execution of the attack. As a result, we found that it is possible to detect the attack by exploiting three kinds of important factors. Then, we constructed a detection algorithm based on the experimental results. Our algorithm utilizes machine learning techniques including a logistic regression and ANN(Artificial Neural Network) to learn from different execution environments. Evaluation shows that the algorithm successfully detects all kinds of attacks with relatively low false rate.
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Å°¿öµå(Keyword) |
ij½Ã ºÎä³Î °ø°Ý
FLUSH+RELOAD °ø°Ý
Performance Counter Monitor
°ø°Ý ŽÁö
Cache-Side Channel Attack
FLUSH+RELOAD Attack
Performance Counter Monitor
Attack Detection
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