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    ๋„ฅ์Šจ์˜ ์—”์”จ์†Œํ”„ํŠธ ์ฃผ์‹๋งค์ž…์— ๋”ฐ๋ฅธ ๊ฒฝ์Ÿํšจ๊ณผ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์ œํ•™๋ถ€, 2013. 2. ์ด์ƒ์Šน.์ตœ๊ทผ ๋„ฅ์Šจ์ด ์—”์”จ์†Œํ”„ํŠธ์˜ ์ฃผ์‹์„ ๋งค์ž…ํ•˜์˜€๋‹ค. ๋„ฅ์Šจ์€ ์„ธ๊ณ„ ๊ฒŒ์ž„์‹œ์žฅ์—์„œ ๊ฒฝ์Ÿ๋ ฅ์„ ๊ฐ–์ถ”๊ธฐ ์œ„ํ•ด ๋ถˆ๊ฐ€ํ”ผํ•œ ํ–‰๋™์ด๋ผ ์ฃผ์žฅํ•˜์˜€์ง€๋งŒ, ์ด๋ฒˆ ์ฃผ์‹๋งค์ž…์€ ํ•œ ์‚ฐ์—…๊ตฐ์—์„œ 1, 2์œ„ ๊ธฐ์—… ๊ฐ„์˜ ๊ฑฐ๋ž˜์ด๊ธฐ ๋•Œ๋ฌธ์— ์‹œ์žฅ๊ฒฝ์Ÿ์ด ์ €ํ•˜๋  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ์‚ฌ๊ฑด์ด์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฒŒ์ž„์‹œ์žฅ์„ ์ „์ฒด๊ฒŒ์ž„์‹œ์žฅ, RPG๊ฒŒ์ž„์‹œ์žฅ, ์ผ€์ฃผ์–ผ(FPSํฌํ•จ)๊ฒŒ์ž„์‹œ์žฅ์œผ๋กœ ๋ถ„๋ฆฌํ•˜๊ณ , ์ด๋ฒˆ ์‚ฌ๊ฑด์„ ํ†ตํ•˜์—ฌ ๋„ฅ์Šจ๊ณผ ์—”์”จ์†Œํ”„ํŠธ๊ฐ€ ๊ธฐ์—…๊ฒฐํ•ฉ์ด ๋˜์—ˆ์„ ์‹œ๋ฅผ ๊ฐ€์ •ํ•ด, ๊ฐ๊ฐ์˜ ์‹œ์žฅ์—์„œ ์ง‘์ค‘๋„๊ฐ€ ์–ด๋Š ์ •๋„ ๋†’์•„์กŒ๋Š”์ง€ HHI์ง€์ˆ˜๋ฅผ ํ†ตํ•˜์—ฌ ์•Œ์•„๋ณด์•˜๊ณ , ๊ทธ ๊ฒฐ๊ณผ ์ „์ฒด๊ฒŒ์ž„์‹œ์žฅ๊ณผ RPG๊ฒŒ์ž„์‹œ์žฅ ์ƒ๋‹นํžˆ ์‹œ์žฅ๊ฒฝ์Ÿ๋„๊ฐ€ ์ €ํ•˜ ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์„ ํ†ตํ•˜์—ฌ ๋น„๋ก ๊ณต์ •๊ฑฐ๋ž˜๋ฒ•์— ์œ„๋ฐ˜๋œ ํ–‰๋™์€ ์•„๋‹ˆ์ง€๋งŒ ์ฃผ์‹ ๋งค์ž…์„ ํ†ตํ•ด ๋‘ ๊ธฐ์—…์ด ํ•˜๋‚˜์˜ ๊ธฐ์—…์ฒ˜๋Ÿผ ํ–‰๋™์„ ํ•  ๊ฒฝ์šฐ ์‹œ์žฅ๊ฒฝ์Ÿ๋„ ์ƒ๋‹นํžˆ ๋‚ฎ์•„์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๋‹ค.โ… . ์„œ๋ก  4 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  4 2. ์„ ํ–‰์—ฐ๊ตฌ 6 โ…ก. ๋„ฅ์Šจ์˜ ์—”์”จ์†Œํ”„ํŠธ ์ฃผ์‹๋งค์ž… ๋ฐฐ๊ฒฝ 7 โ…ข. ๊ด€๋ จ์‹œ์žฅ ๊ทœ์ • 12 1. ๋„ฅ์Šจ๊ณผ ์—”์”จ์†Œํ”„ํŠธ ์†Œ๊ฐœ 12 (1) ์—”์”จ์†Œํ”„ํŠธ 13 (2) ๋„ฅ์Šจ 13 2. ์ „์ฒด ๊ฒŒ์ž„์‹œ์žฅ 14 3. RPG ๊ฒŒ์ž„์‹œ์žฅ 19 4. ์ผ€์ฃผ์–ผ(FPSํฌํ•จ) ๊ฒŒ์ž„์‹œ์žฅ 24 5. ์ˆ˜์š”๋Œ€์ฒด์„ฑ๊ณผ ๊ณต๊ธ‰๋Œ€์ฒด์„ฑ 28 โ…ฃ. ์‹œ์žฅ์ง‘์ค‘๋„ 31 1. ์ „์ฒด ๊ฒŒ์ž„์‹œ์žฅ 32 2. RPG ๊ฒŒ์ž„์‹œ์žฅ 37 โ…ค. ๊ฒฝ์Ÿ์ œํ•œ ํšจ๊ณผ 44 1. ๋‹จ๋…ํšจ๊ณผ 44 (1) ๊ฒฝ์Ÿ์‚ฌ์—…์ž ๋ฐฐ์ œ ๊ฐ€๋Šฅ์„ฑ 44 (2) ์ง„์ž…์žฅ๋ฒฝ ์ฆ๋Œ€ ๊ฐ€๋Šฅ์„ฑ 45 (3) ๊ฐ€๊ฒฉ์ธ์ƒ ๊ฐ€๋Šฅ์„ฑ 46 2. ์กฐ์ •ํšจ๊ณผ 48 โ…ฅ. ํšจ์œจ์„ฑ ์ฆ๋Œ€ํšจ๊ณผ 50 โ…ฆ. ๊ฒฐ๋ก  53 ์ฐธ๊ณ ๋ฌธํ—Œ 56 Abstract 59Maste

    ํƒ„์ˆ˜ํ™”๋ฌผ๊ธฐ๋ฐ˜ ๋ฐ”์ด์˜ค๋งค์Šค ์ „ํ™˜์„ ์œ„ํ•œ ๋ถˆ๊ท ์ผ๊ณ„ ์ฒ ๊ณผ ๋ฃจํ…Œ๋Š„ ์ด‰๋งค์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2015. 2. ์ด์œค์‹.Heterogeneous catalyst applications have increased attention in green chemistry. Polymer, metal oxide, and carbon are used as universal support materials to fabricate heterogeneous catalysts. Because the heterogeneous catalyst is generally insoluble in solvent, it can be separated easily from the reaction media by filtration and recycled numerous times. Moreover, it is possible to design the property of catalysts through chemical processes. Despite these advantages, the use of heterogeneous catalysts has not been increased in biomass conversion to produce value-added chemicals because of lower activities than homogeneous catalysts. Thus, the development of highly efficient heterogeneous catalyst still remains a challenge in biomass transformation. In this thesis, two types of heterogeneous transition metal catalysts were utilized for efficient carbohydrate transformations: iron catalyst for fructose dehydration and ruthenium catalyst for 5-hydroxymethyl furfural (HMF) hydrogenation/oxidation. In chapter 1, fructose dehydration into HMF by heterogeneous NHC-Fe catalyst is described. Developing HMF production process has been the most important task in C6 biomass transformation. So far, numerous HMF production methods from carbohydrates have been reported using various catalysts such as Bronsted and Lewis acid, and ionic liquids. N-heterocyclic carboene (NHC)-Fe complex grafted heterogeneous catalysts were prepared from polystyrene and graphene oxide support. NHC ligand provided a stable and strong metal-ligand bonding environment to form an efficient active site. Polystyrene supported NHC-FeIII catalyst showed excellent fructose dehydration activity comparing to other supported NHC-metal catalysts. Graphene oxide grafted with NHC-FeIII catalyst showed better catalytic performance than polystyrene supported one. Futhermore, the NHC-FeIII grafted catalysts could be reused for 5 cycles without significant loss of activity. In chapter 2, synthesis of HMF derivatives by heterogeneous ruthenium catalyst is described. HMF derivatives such as 2,5-furandicarboxylic acid (FDCA) and 2,5-bis(hydroxymethyl)furan (BHMF) have received a lot of attention because of their applicability. Zirconia was chosen as a suitable solid support with unique surface features, which can improve the catalytic ability of the metal active sites. Ruthenium active sites were immobilized on the surface of zirconia without forming Ru(0) metal or RuO2 nanoparticles (NPs). The well deposited Ru active site resulted in enhanced HMF hydrogenation and oxidation activity. The zirconia supported ruthenium catalyst showed excellent catalytic ability for oxidation of HMF into FDCA with molecular oxygen as a green oxidant. HMF was selectively reduced to BHMF with excellent yield under pressured hydrogen gas condition. Moreover, the zirconia supported ruthenium catalyst could be recycled over 10 times without significant loss of activity.TABLE OF CONTENTS ABSTRACT i TABLE OF CONTENTS iv LIST OF TABLES vii LIST OF FIGURES viii LIST OF SCHEME xii LIST OF ABBREVIATIONS xiv Introduction 1. Concept of Green Chemistry 1 2. Biomass and Bio-refinery 4 2.1 Biomass: Renewable Carbon Source 4 2.2 Bio-refinery 6 2.2.1 Strategies of Biomass Transformation Processes. 8 2.2.2 Biomass Based Platform Chemicals 10 2.3 Carbohydrate Biomass Conversion to Produce Platform Chemicals 12 2.3.1 Carbohydrate based Platform Chemicals 12 2.3.2 Synthesis of HMF from Carbohydrates 14 2.3.3 Synthesis of FDCA from HMF 17 2.3.4 Synthesis of BHMF from HMF 21 3. Catalysts 23 3.1 Heterogeneous Catalysts 23 3.2 Catalytic Dehydration Reaction 25 3.3 Catalytic Oxidation Reaction 26 3.4 Catalytic Reduction Reaction 31 3.5 Iron Catalyst in Organic Synthesis 33 3.6 Ruthenium Catalyst in Organic Synthesis 36 4. Research Objectives 38 Chapter 1 Fructose Dehydration for Producing HMF 1. Experimental Section 39 1.1. Chemicals and Materials 39 1.2 Characterization 41 1.3. Preparation of N-Heterocyclic Carbene (NHC)โ”€FeIII Catalyst 43 1.3.1 Immobilization of 1-Methyl Imidazole on Chloromethyl Polystyrene 43 1.3.2 Preparation of PS-NHC-FeIII Catalyst 44 1.3.3 Synthesis of NHC Ligand 45 1.3.4. Preparation of Graphene Oxide (GO) 47 1.3.5 Preparation of GO-NHC-FeIII 48 1.4. Fructose Dehydration Reaction Catalyzed by NHC-FeIII 49 1.4.1. General Experimental Procedure for Fructose Dehydration Reaction Catalyzed by PS-NHC-FeIII 49 1.4.2 General Experimental Procedure for Fructose Dehydration Reaction Catalyzed by GO-NHC-FeIII 50 1.4.3 Reusability Test of PS-NHC-FeIII Catalysts for Fructose Dehydration Reaction 51 1.4.4 Reusability Test of GO-NHC-FeIII Catalysts for Fructose Dehydration Reaction 52 1.5 Separation of HMF from DMSO 53 2. Result and Discussion 54 2.1. Characterization of PS-NHC-FeIII 54 2.2. Dehydration of Fructose to HMF Catalyzed by PS-NHC-Metal Catalysts 65 2.3. Characterization of GO-NHC-FeIII 71 2.4. Dehydration of fructose to HMF catalyzed by GO-NHC-FeIII catalysts 83 2.5. Separation of HMF from DMSO 89 3. Conclusion 91 Chapter 2. Conversion of HMF to Value Added Chemicals 1. Experimental Section 92 1.1. Chemicals and Materials 92 1.2. Characterization 93 1.3. Oxidation of HMF into FDCA Catalyzed by Ru(OH)x/ZrO2 95 1.4. Reduction of HMF into BHMF Catalyzed by Ru(OH)x/ZrO2 96 2. Results and Discussion 97 2.1. Characterization of Ru(OH)x/ZrO2 Catalyst 97 2.2. Oxidation HMF into FDCA Catalyzed by Ru(OH)x/ZrO2 103 2.3. Reduction HMF into BHMF Catalyzed by Ru(OH)x/ZrO2 118 2.3.1. Reaction Optimization in Reduction HMF into BHMF 118 2.3.2. Hydrogenation of HMF into BHMF 123 3. Conclusion 129 References 130 Appendix 151 Abstract in Korean 161Docto

    1492, ์ฝœ๋Ÿผ๋ฒ„์Šค ๊ทธ๋ฆฌ๊ณ  2009, ์šฐ๋ฆฌ : ์˜ํ™”ใ€ˆ1492. ๋‚™์›์˜ ์ •๋ณตใ€‰์„ ํ†ตํ•ด ๋ณธ ์ฝœ๋Ÿผ๋ฒ„์Šค ๊ทธ๋ฆฌ๊ณ  ์šฐ๋ฆฌ

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    ์ง€๊ตฌ๋Š” ์—ฌ๊ธฐ์„œ ๋๋‚œ๋‹ค(No More Ahead). ๊ด‘๋Œ€ํ•œ ๋ฐ”๋‹ค๋กœ ๋‚˜ ๊ฐ€๋Š” ๋ฐ”๋‹ท๊ธธ ์ดˆ์ž…์— ๊ฑฐ๋Œ€ํ•œ ๊ธ€๊ท€๊ฐ€ ๋ฒฝ์ฒ˜๋Ÿผ ๊ฐ€๋กœ๋ง‰ํ˜€ ์žˆ๋‹ค. ์ปค๋‹ค ๋ž€ ๋ฒ”์„  ํ•œ ์ฒ™์ด ์ด์— ์•„๋ž‘๊ณณํ•˜์ง€ ์•Š๊ณ  ๋ฒฝ์„ ํ–ฅํ•ด ํ•ญํ•ด๋ฅผ ๊ณ„์†ํ•˜ ๋”๋‹ˆ ๊ธฐ์–ด์ด No๋ผ๋Š” ๊ธ€๊ท€๋ฅผ ๋ฌด๋„ˆ๋œจ๋ฆฌ๋ฉฐ ๋จผ ๋ฐ”๋‹ค๋กœ ๋‚˜์•„๊ฐ„๋‹ค. ๋‚จ ์€ ๊ธ€๊ท€๋Š” More Ahead. ๋” ํฐ ์„ธ์ƒ์„ ๋ฐœ๊ฒฌํ•  ๊ฒƒ์ด๋‹ค๋ผ๋Š” ๋ฉ” ์‹œ์ง€๋‹ค. ์–ผ๋งˆ ์ „๋ถ€ํ„ฐ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ํ•œ ํ†ต์‹ ์—…์ฒด์—์„œ ๊ธฐ์—… ์ด๋ฏธ์ง€ ๊ด‘๊ณ ๋กœ ๋‚ด๋ณด๋‚ด๊ณ  ์žˆ๋Š” ์˜์ƒ์ด๋‹ค. ๋งํ•  ๋‚˜์œ„ ์—†์ด, 1492๋…„ ์ฝœ๋Ÿผ๋ฒ„์Šค์˜ ์‹ ๋Œ€๋ฅ™ ๋ฐœ๊ฒฌ์— ์ฐฉ์•ˆํ•œ ๊ด‘๊ณ ๋‹ค. 100๋ฏธํ„ฐ์— ๋‹ฌํ•˜๋Š” ๋ฒ”์„ ์„ ์‹ค์ œ๋กœ ๋งŒ๋“ค๊ณ  100์—ฌ ๋ช…์˜ ๋ฑƒ ์‚ฌ๋žŒ์ด ํƒ‘์Šนํ•ด ํ•ญํ•ดํ–ˆ๋Š”๊ฐ€ ํ•˜๋ฉด ํ•ญ๊ณต์ดฌ์˜์„ ์œ„ํ•ด ํ—ฌ๊ธฐ๋ฅผ ๋™์›ํ•˜ ๋Š” ๋“ฑ ํ•œ ํŽธ์˜ ๋ธ”๋ก๋ฒ„์Šคํ„ฐ ์˜ํ™”๋ฅผ ์ฐ๋“ฏ ๊ด‘๊ณ ๋ฅผ ์ œ์ž‘ํ–ˆ๋‹ค๋Š” ํ›„๋ฌธ ์ด๋‹ค. ๊ธฐ์กด์˜ ํ•œ๊ณ„๋ฅผ ๋ฒ—์–ด๋‚˜ ๋” ํฐ ์„ธ์ƒ์œผ๋กœ ๋‚˜์•„๊ฐ„ ์ฝœ๋Ÿผ๋ฒ„์Šค์˜ ์ •์‹ ์„ ์ด์–ด๋ฐ›์•„, ์˜ค๋Š˜์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ  ์ƒˆ๋กœ์šด ์‹ ํ™” ์ฐฝ์กฐ์— ๋‚˜์„œ๊ฒ ๋‹ค๋Š” ๊ฒƒ์ด ๊ธฐ์—…์ด ๋ฐํžŒ ๊ด‘๊ณ ์˜ ๊ธฐํš ์˜๋„๋‹ค

    ์ž๊ธฐ ๊ตฌ์›๊ณผ ์ž๊ธฐ ํŒŒ๊ดด - ์†Œ๋ฅด ํ›„์•„๋‚˜์™€ ์ „ํ˜œ๋ฆฐ

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    ์‹ ์ข… ์ธํ”Œ๋ฃจ์—”์ž A(์‹ ์ข… ํ”Œ๋ฃจ)๊ฐ€ ๋Œ€์œ ํ–‰์ด๋‹ค. 2009๋…„ 4์›” ์ฒ˜ ์Œ ๋ฉ•์‹œ์ฝ”์—์„œ ์ฒซ ์‚ฌ๋ง์ž๊ฐ€ ๋ฐœ์ƒํ•˜์—ฌ ๋ฏธ๊ตญ, ์บ๋‚˜๋‹ค ๋“ฑ์œผ๋กœ๊นŒ์ง€ ํ™•์‚ฐ๋œ ์‹ ์ข… ํ”Œ๋ฃจ๋Š” ์–ผ๋งˆ ์ „๋ถ€ํ„ฐ๋Š” ์šฐ๋ฆฌ๋‚˜๋ผ์—์„œ๋„ ๊ทธ ๊ธฐ์„ธ๊ฐ€ ๋งŒ ๋งŒ์น˜ ์•Š๋‹ค. ํ•˜๋ฃจ์—๋„ ๋ช‡ ๋ช…์˜ ๋ชฉ์ˆจ์„ ์•—์•„๊ฐ€๋ฉฐ ๋ฒŒ์จ ๋งˆํ” ๋ช…์ด ๋„˜๋Š” ์‚ฌ๋ง์ž๊ฐ€ ๋‚˜์™”๋‹ค. ์‹œ์›” ๋ง๊นŒ์ง€ ์ „ ์„ธ๊ณ„์ ์œผ๋กœ 4์ฒœ์—ฌ ๋ช…์˜ ์‚ฌ๋ง์ž๊ฐ€ ๋ฐœ์ƒํ•œ ๊ฒƒ์œผ๋กœ ์„ธ๊ณ„๋ณด๊ฑด๊ธฐ๊ตฌ๋Š” ๋ฐํ˜”๋‹ค. ์‹œ์‹œ๊ฐ๊ฐ ์ฃฝ์Œ ์˜ ์œ„ํ˜‘์ด ์šฐ๋ฆฌ ์•ž์— ๋„์‚ฌ๋ฆฌ๊ณ  ์žˆ๋Š” ๊ฒƒ์ด๋‹ค..

    Neural Network-Based Anomaly Detection Using Integrated Sequence of System Call Function and Argument

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2021. 2. ๋ฐฑ์œคํฅ.์‹œ์Šคํ…œ ํ˜ธ์ถœ ํ•จ์ˆ˜ ์‹œํ€€์Šค๋งŒ์„ ๋ฐ์ดํ„ฐ๋กœ ํ™œ์šฉํ•˜์—ฌ ์‹œ์Šคํ…œ์˜ ์ •์ƒ์ ์ธ ํ–‰์œ„๋ฅผ ํ•™์Šตํ•˜๋Š” ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜ ์ด์ƒ ํƒ์ง€ ๋ชจ๋ธ์˜ ๋ฌธ์ œ์  ์ค‘ ํ•˜๋‚˜๋Š” mimicry ๊ณต๊ฒฉ์— ์ทจ์•ฝํ•˜๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๊ณต๊ฒฉ์ž๋Š” ๋ฐ์ดํ„ฐ์— no-op ์‹œ์Šคํ…œ ํ˜ธ์ถœ์„ ์‚ฝ์ž…ํ•˜๋Š” ๋“ฑ์˜ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ณต๊ฒฉ ํ–‰์œ„๋ฅผ ์ •์ƒ์ ์ธ ํ–‰์œ„์ธ ๊ฒƒ์ฒ˜๋Ÿผ ๋ชจ๋ฐฉํ•˜์—ฌ ํƒ์ง€๋ฅผ ํ”ผํ•˜๊ณ  ์›ํ•˜๋Š” ๊ณต๊ฒฉ์„ ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๊ฒŒ ๋œ๋‹ค. ์ด๋Ÿฐ mimicry ๊ณต๊ฒฉ์„ ๋ง‰๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•๋ก ์ด ์ œ์‹œ๋˜์—ˆ์œผ๋ฉฐ ๊ทธ ์ค‘ ๋Œ€ํ‘œ์ ์ธ ๊ฒƒ์ด ์‹œ์Šคํ…œ ํ˜ธ์ถœ ํ•จ์ˆ˜ ๋Œ€์‹  branch ์‹œํ€€์Šค๋ฅผ ๋ฐ์ดํ„ฐ๋กœ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ branch ์‹œํ€€์Šค๋Š” ์‹œ์Šคํ…œ ํ˜ธ์ถœ ํ•จ์ˆ˜์— ๋น„ํ•ด ๋ฐ์ดํ„ฐ์˜ ์–‘์ด ๋„ˆ๋ฌด ๋งŽ์•„ ๊ณต๊ฒฉ ํƒ์ง€ ์†Œ์š” ์‹œ๊ฐ„์ด ์˜ค๋ž˜ ๊ฑธ๋ฆฌ๋ฉฐ ๋ณ„๋„์˜ ํ•˜๋“œ์›จ์–ด ํ”ผ์ณ๋“ค์„ ํ™œ์šฉํ•ด ์ถ”์ถœํ•ด์•ผ ํ•œ๋‹ค๋Š” ๋ฒˆ๊ฑฐ๋กœ์›€์ด ์žˆ์–ด ์‹ค์‹œ๊ฐ„ ์˜จ๋ผ์ธ ํƒ์ง€ ์‹œ์Šคํ…œ์— ์ ์šฉํ•˜๊ธฐ์—” ์ ํ•ฉํ•˜์ง€ ์•Š๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฐ ๋‹จ์ ๋“ค์„ ํ•ด์†Œํ•˜๋Š” ๋™์‹œ์— mimicry ๊ณต๊ฒฉ์— ๊ฐ•๊ฑดํ•œ ๋ชจ๋ธ์„ ๋งŒ๋“ค๊ธฐ ์œ„ํ•ด ๊ธฐ์กด์— ์‚ฌ์šฉํ•˜๋˜ ์‹œ์Šคํ…œ ํ˜ธ์ถœ ํ•จ์ˆ˜ ๋ฐ์ดํ„ฐ์™€ ์‹œ์Šคํ…œ ํ˜ธ์ถœ ์ธ์ž ๋ฐ์ดํ„ฐ๋ฅผ ํ•จ๊ป˜ ํ†ตํ•ฉ์‹œ์ผœ ํ™œ์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ์‹œ์Šคํ…œ ํ˜ธ์ถœ ์ธ์ž๊ฐ€ ๊ณต๊ฒฉ์„ ํƒ์ง€ํ•˜๊ธฐ ์œ„ํ•œ ์‹œ์Šคํ…œ ํ–‰์œ„ ์ •๋ณด๋ฅผ ์ถฉ๋ถ„ํžˆ ๋‹ด๊ณ  ์žˆ์Œ์„ ๋ณด์ด๋Š” ํ†ต๊ณ„ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์ ‘๊ทผ๋ฒ•์— ๊ธฐ์ธํ•˜์—ฌ ์‹œ์Šคํ…œ ํ˜ธ์ถœ ์ธ์ž ๋ฐ์ดํ„ฐ๋ฅผ ๋”ฅ๋Ÿฌ๋‹ ๋ชจ๋ธ์˜ ์ž…๋ ฅ ๋ฒกํ„ฐ๋กœ ๋ณ€ํ™˜์‹œํ‚จ๋‹ค. ์ดํ›„ ์‹œ์Šคํ…œ ํ˜ธ์ถœ ํ•จ์ˆ˜์˜ ์ž…๋ ฅ ๋ฒกํ„ฐ์™€ ํ†ตํ•ฉํ•˜์—ฌ LSTM ๋ชจ๋ธ์„ ํ†ตํ•ด ๊ณต๊ฒฉ์„ ํƒ์ง€ํ•˜๋Š” ๊ฒƒ์ด ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ๊ธฐ๋ฒ•์ด๋‹ค. ์ œ์•ˆ๋œ ๋ชจ๋ธ์˜ ์„ฑ๋Šฅ์„ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•ด branch ์‹œํ€€์Šค๋ฅผ ๋ฐ์ดํ„ฐ๋กœ ํ™œ์šฉํ•œ ์—ฐ๊ตฌ์™€ ๋น„๊ต ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋‘ ๊ฐœ์˜ ์‹ค์ œ ํ”„๋กœ๊ทธ๋žจ์„ ๋Œ€์ƒ์œผ๋กœ ๊ณต๊ฒฉ์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ๊ณต๊ฒฉ์ด ์„ฑ๊ณต์ ์œผ๋กœ ํƒ์ง€๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, branch ์‹œํ€€์Šค ๋ชจ๋ธ๊ณผ ๋น„๊ตํ•˜์—ฌ ํƒ์ง€ ์ •ํ™•๋„์™€ ํƒ์ง€ ์†Œ์š” ์‹œ๊ฐ„, ์–‘์ชฝ ์ง€ํ‘œ ๋ชจ๋‘ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋˜์—ˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.One of the problems with neural network-based anomaly detection models that use only system call function sequences as data to learn the normal behavior of the system is that they are vulnerable to mimicry attacks. This allows the attacker can imitate an attack as if it were a normal behavior by inserting no-op system calls into the data to avoid detection and carry out the desired attack. Various methodologies have been presented to prevent these mimicry attacks, and one of them is to use branch sequences as data instead of system call functions. However, branch sequences have the disadvantage of being difficult to apply to real-time online detection system due to the fact that the amount of data is too large compared to system call functions, which takes much more time to detect attacks and the hassle of extracting them using separate hardware features. To solve these shortcomings and at the same time to create a robust model for mimicry attacks, this paper presents a new methodology for integrating system call function sequence data used previously and system call arguments data together. We converts system call arguments data to the input vector of the deep learning model, referring to the statistical model-based approach that shows that the system call arguments contain enough system behavior information to detect attacks. Then in our technique, we integrate system call function and argument input vectors to detect attacks through LSTM model. To measure the performance of the proposed model, comparative experiment was conducted with studies using the branch sequences as data. We confirmed that the attacks carried out on two real programs were successfully detected, and both detection accuracy and detection time were improved compared with the branch sequence model.๋ชฉ ์ฐจ ์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 2 ์žฅ ๋ฐฐ๊ฒฝ ์ง€์‹ 4 ์ œ 1 ์ ˆ ์‹œ์Šคํ…œ ํ˜ธ์ถœ 4 ์ œ 2 ์ ˆ LSTM 4 ์ œ 3 ์žฅ ๋””์ž์ธ ๋ฐ ๊ตฌํ˜„ 6 ์ œ 1 ์ ˆ ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ ๋ฐ ์ž…๋ ฅ ๋ฒกํ„ฐ ๊ตฌ์„ฑ 6 ์ œ 2 ์ ˆ LSTM ๋ ˆ์ด์–ด 7 ์ œ 3 ์ ˆ ์ถœ๋ ฅ ๋ฒกํ„ฐ ํ•ด์„ 8 ์ œ 4 ์žฅ ์‹คํ—˜ 10 ์ œ 1 ์ ˆ ๋ฐ์ดํ„ฐ ์…‹ ๊ตฌ์„ฑ 10 ์ œ 2 ์ ˆ ๋ชจ๋ธ ํ•™์Šต 11 ์ œ 3 ์ ˆ ๋ชจ๋ธ ํ‰๊ฐ€ 11 ์ œ 5 ์žฅ ์‹คํ—˜ ๊ฒฐ๊ณผ 13 ์ œ 1 ์ ˆ ๊ณต๊ฒฉ ํƒ์ง€ ์ •ํ™•๋„ ๋น„๊ต 13 ์ œ 2 ์ ˆ ๊ณต๊ฒฉ ํƒ์ง€ ์‹œ๊ฐ„ ๋น„๊ต 13 ์ œ 6 ์žฅ ๊ฒฐ๋ก  14 ์ฐธ๊ณ ๋ฌธํ—Œ 15 Abstract 16Maste

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