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Current Result Document : 13 / 17
ÇѱÛÁ¦¸ñ(Korean Title) |
Hybrid Feature Selection°ú Data BalancingÀ» ÅëÇÑ È¿À²ÀûÀÎ ³×Æ®¿öÅ© ħÀÔ Å½Áö ¸ðµ¨ |
¿µ¹®Á¦¸ñ(English Title) |
Improved Network Intrusion Detection Model through Hybrid Feature Selection and Data Balancing |
ÀúÀÚ(Author) |
¹Îº´ÁØ
À¯ÁöÈÆ
½Åµ¿±Ô
½Åµ¿ÀÏ
Byeongjun Min
Jihun Ryu
Dongkyoo Shin
Dongil Shin
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¿ø¹®¼ö·Ïó(Citation) |
VOL 10 NO. 02 PP. 0065 ~ 0072 (2021. 02) |
Çѱ۳»¿ë (Korean Abstract) |
ÃÖ±Ù ³×Æ®¿öÅ© ȯ°æ¿¡ ´ëÇÑ °ø°ÝÀÌ ±Þ¼Óµµ·Î °íµµÈ ¹× Áö´ÉÈ µÇ°í Àֱ⿡, ±âÁ¸ÀÇ ½Ã±×´Ïó ±â¹Ý ħÀÔŽÁö ½Ã½ºÅÛÀº ÇÑ°èÁ¡ÀÌ ¸íÈ®ÇØÁö°í ÀÖ´Ù. ÀÌ·¯ÇÑ ¹®Á¦¸¦ ÇØ°áÇϱâ À§Çؼ ±â°èÇнÀ ±â¹ÝÀÇ Ä§ÀÔ Å½Áö ½Ã½ºÅÛ¿¡ ´ëÇÑ ¿¬±¸°¡ È°¹ßÈ÷ ÁøÇàµÇ°í ÀÖ´Ù. ÇÏÁö¸¸ ±â°èÇнÀÀ» ħÀÔ Å½Áö¿¡ ÀÌ¿ëÇϱâ À§Çؼ´Â µÎ °¡Áö ¹®Á¦¿¡ Á÷¸éÇÑ´Ù. ù ¹ø°´Â ½Ç½Ã°£ ŽÁö¸¦ À§ÇÑ ÇнÀ°ú ¿¬°üµÈ Áß¿ä Ư¡µéÀ» ¼±º°ÇÏ´Â ¹®Á¦À̸ç, µÎ ¹ø°´Â ÇнÀ¿¡ »ç¿ëµÇ´Â µ¥ÀÌÅÍÀÇ ºÒ±ÕÇü ¹®Á¦·Î, ±â°èÇнÀ ¾Ë°í¸®ÁòµéÀº µ¥ÀÌÅÍ¿¡ ÀÇÁ¸ÀûÀ̱⿡ ÀÌ·¯ÇÑ ¹®Á¦´Â Ä¡¸íÀûÀÌ´Ù. º» ³í¹®¿¡¼´Â À§ Á¦½ÃµÈ ¹®Á¦µéÀ» ÇØ°áÇϱâ À§Çؼ Hybrid Feature Selection°ú Data BalancingÀ» ÅëÇÑ ½ÉÃþ ½Å°æ¸Á ±â¹ÝÀÇ ³×Æ®¿öÅ© ħÀÔ Å½Áö ¸ðµ¨ÀÎ HFS-DNNÀ» Á¦¾ÈÇÑ´Ù. NSL-KDD µ¥ÀÌÅÍ ¼ÂÀ» ÅëÇØ ÇнÀÀ» ÁøÇàÇÏ¿´À¸¸ç, ±âÁ¸ ºÐ·ù ¸ðµ¨µé°ú ¼º´É ºñ±³¸¦ ¼öÇàÇÑ´Ù. º» ¿¬±¸¿¡¼ Á¦¾ÈµÈ Hybrid Feature Selection ¾Ë°í¸®ÁòÀÌ ÇнÀ ¸ðµ¨ÀÇ ¼º´ÉÀ» ¿Ö°î ½ÃÅ°Áö ¾Ê´Â °ÍÀ» È®ÀÎÇÏ¿´À¸¸ç, ºÒ±ÕÇüÀ» ÇؼÒÇÑ ÇнÀ ¸ðµ¨µé°£ ½ÇÇè¿¡¼ º» ³í¹®¿¡¼ Á¦¾ÈÇÑ ÇнÀ ¸ðµ¨ÀÌ °¡Àå ÁÁÀº ¼º´ÉÀ» º¸¿´´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Recently, attacks on the network environment have been rapidly escalating and intelligent. Thus, the signature-based network intrusion detection system is becoming clear about its limitations. To solve these problems, research on machine learning-based intrusion detection systems is being conducted in many ways, but two problems are encountered to use machine learning for intrusion detection. The first is to find important features associated with learning for real-time detection, and the second is the imbalance of data used in learning. This problem is fatal because the performance of machine learning algorithms is data-dependent. In this paper, we propose the HSF-DNN, a network intrusion detection model based on a deep neural network to solve the problems presented above. The proposed HFS-DNN was learned through the NSL-KDD data set and performs performance comparisons with existing classification models. Experiments have confirmed that the proposed Hybrid Feature Selection algorithm does not degrade performance, and in an experiment between learning models that solved the imbalance problem, the model proposed in this paper showed the best performance.
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Å°¿öµå(Keyword) |
ħÀÔ Å½Áö
µö ·¯´×
¿À¹ö»ùÇøµ
Ư¡ ¼±ÅÃ
Intrusion Dectection
Deep Learning
Over Sampling
Feature Selection
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