272 research outputs found
1982β2021λ κΈ°κ° λνν΄ν μ μΈ΅λμμ μ₯κΈ° λ³λμ±
νμλ
Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : μμ°κ³Όνλν μ§κ΅¬νκ²½κ³ΌνλΆ, 2023. 8. λνλ.The Korea Strait serves as an inlet of the East Sea since the Tsushima Warm Current, a branch of the Kuroshio Current, flows into it through the Korea Strait. The major water masses in the Korea Strait include the Tsushima Warm Water (with temperatures above 10Β°C and salinity exceeding 34.3 psu), transported by the Tsushima Warm Current, and the Korea Strait Bottom Cold Water (KSBCW) characterized by temperatures below 10Β°C, predominantly found at greater depths in the western channel of the Strait. In this study, long-term variations of the KSBCW were analyzed using in situ temperature data obtained from the Korea Oceanographic Data Center from 1982 to 2021. The vertical temperature section of the Korea Strait exhibits an interannual variation of the KSBCW as its primary mode of variability on time scales longer than the seasonal cycle. The second most significant mode reflects an enhancement of both the KSBCW and the Tsushima Warm Water since the mid-1990s, implying a strengthening of vertical stratification. The first mode shows a significant relationship with the upper-layer water temperature variability in the southwestern part of the East Sea. The regression analysis using the ERA5 wind fields targeting the first mode reveals that the KSBCW is strengthened, associated with basin-scale counterclockwise wind-stress curl anomalies. The second mode, however, is suggested to be related to clockwise wind-stress curl anomalies. The relationship between the KSBCW variability and the basin-scale wind variations suggests future works exploring the link with larger-scale climate variations in the broader North Pacific.λνν΄νμ μΏ λ‘μμ€ ν΄λ₯μ μ§λ₯μΈ μ°μλ§ λλ₯κ° λν΄λ‘ μ μ
λλ ν΅λ‘μ΄λ€. λνν΄νμ μ£Όμ μκ΄΄λ‘λ μ°μλ§ λλ₯κ° μμ‘νλ μ°μλ§ μ¨λμ(μμ¨ 10β μ΄μ, μΌλΆ 34.3 psu μ΄μ)μ, μ£Όλ‘ μμλ κΉμ μμ¬μμ λ°κ²¬λλ μμ¨ 10β μ΄νμ λνν΄ν μ μΈ΅λμκ° μλ€. μ΄ μ°κ΅¬μμλ 1982λ
λΆν° 2021λ
κΉμ§μ κΈ°κ° λμ νκ΅ν΄μμλ£μΌν°μμ μ 곡νλ μ μ ν΄μκ΄μΈ‘ μμ¨ μλ£λ₯Ό μ¬μ©νμ¬ λνν΄ν μ μΈ΅λμμ μ₯κΈ° λ³λμ λΆμνκ³ , λν΄ λ¨μμͺ½ μΈλ¦λΆμ§ μμΈ΅ μμ¨ λ³λκ³Όμ κ΄κ³λ₯Ό μ΄ν΄λ³΄μλ€. λν ERA5 λ°λ μλ£λ₯Ό μ¬μ©νμ¬ λΆμ§κ·λͺ¨ λ°λμ₯κ³Όμ μκ΄κ΄κ³λ₯Ό λΆμνμλ€. 208 μ μ λνν΄ν μμ¨ μμ§ λ¨λ©΄ λΆμ κ²°κ³Ό κ³μ λ³λλ³΄λ€ κΈ΄ μ£ΌκΈ°μμ κ°μ₯ μ£Όμν λͺ¨λκ° λνν΄ν μ μΈ΅λμμ κ²½λ
λ³λμΈ κ²μΌλ‘ λνλ¬λ€. μ΄ λͺ¨λμ νκ·λΆμν λ°λμ λΆμ§ κ·λͺ¨μμ λ°μκ³ λ°©ν₯μ νμ μ±λΆμ 보μλ€. λλ²μ§Έλ‘ μ£Όμν λͺ¨λλ 1990λ
λ μ΄ν λνν΄ν μ μΈ΅λμμ μ°μλ§ μ¨λμκ° λͺ¨λ κ°νλλ μμ§ μ±μΈ΅μ΄ κ°νλ₯Ό λνλλ€. μ΄ λͺ¨λμ νκ·λΆμν λ°λμ λΆμ§ κ·λͺ¨μμ μκ³ λ°©ν₯μ νμ μ±λΆμ 보μλ€. μ΄λ¬ν λΆμ§ κ·λͺ¨ λ°λμ₯κ³Όμ μκ΄μ±μ λ°νμΌλ‘, μμΌλ‘μ μ°κ΅¬μμλ λνν΄ν μ μΈ΅λμμ μ₯κΈ° λ³λμ΄ λΆννμ κΈ°ν λ³λκ³Ό μ΄λ ν μκ΄μ±μ κ°μ§λμ§μ λν λΆμμ΄ νμν κ²μΌλ‘ μκ°λλ€.Abstract i
Table of Contents iii
List of Figures iv
1. Introduction 1
1.1. Background 1
1.2. Objectives 7
2. Data and Methods 8
2.1. Data 8
2.2. Methods 9
3. Results 13
3.1. Climatology 13
3.2. KSBCW in Summer 15
3.3. First EOF mode 16
3.4. Second EOF mode 21
3.5. Relationship with wind variability 26
4. Discussion and Conclusion 30
Bibliography 36
Abstract in Korean 39μ
ν΄λ§ νμ μμ CA1κ³Ό CA3μ μ₯λ©΄ μκ·Ήμ κΈ°λ°ν μ₯μ νμ νμ± μ°κ΅¬
νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : μμ°κ³Όνλν λμΈμ§κ³Όνκ³Ό, 2021. 2. μ΄μΈμ.When we recall the past experiences, we usually think of a scene which is a combination of what we saw, the sounds we hear, and the feeling we felt at that moment. Since the scene is an essential component of episodic memory, studying how scene stimuli are represented and stored in the brain is important in understanding the processes of formation, storage, and retrieval of our memories. One of the brain regions important for episodic memory is the hippocampus. It has been reported that patients or animals with damage to the hippocampus have trouble with retrieving past experiences or forming new memories. The hippocampus is involved not only in episodic memory but also in the formation of a cognitive map. In particular, the place cells observed in the rodent hippocampus play a key role in these functions. However, research on place cells has mainly focused on the firing patterns of cells during foraging in a space, and it has not been clear how hippocampal cells represent and make use of visual scenes for behavior.
To find how scene stimuli are represented in place cells, I measured spiking activities of single neurons in the CA1, one of the subregions of hippocampus, and the subiculum, a major output of the hippocampus. Neuronal spiking activity was monitored when the rat performed a task of selecting right or left associated to the scene stimulus presented on monitors. As a result, I found that the place cells in the CA1 and subiculum showed rate modulation according to the scene stimulus. In addition, I also conducted an experiment using a virtual reality system to investigate the neural mechanisms of the formation of a place field based on visual scenes. In this experiment, the rat ran on a virtual linear track as visual cues were added one by one to make a scene-like environment. Neuronal activities of place cells were recorded in the CA1 and CA3 simultaneously to study the neural mechanisms of the development of a place field on the basis of external visual stimuli. Place fields appeared in the CA1 even with a single visual cue, whereas in the CA3, place fields only emerged when a sufficient number of visual cues were collectively arranged in a scene-like fashion. The results suggest that that scene is one of the key stimulus that effectively recruits the hippocampus.μ°λ¦¬λ κ³Όκ±°μ κ²½νμ λ μ¬λ¦΄ λ κ·Έ λλ₯Ό λ¬μ¬νλ λ¬Έμ₯μ λ μ¬λ¦¬λ κ²μ΄ μλλΌ κ²½ν ν μκ°μ 보μλ κ², λ€λ Έλ μ리, λκΌλ κ°μ λ±μ΄ 볡ν©μ μΌλ‘ μ΄μ°λ¬μ§ μ₯λ©΄μ λ μ¬λ¦¬κ² λλ€. μ΄λ κ² μ₯λ©΄μ μΌν κΈ°μ΅μ ꡬμ±νλ μ€μν μμλΌ ν μ μκΈ°μ μ₯λ©΄ μκ·Ήμ΄ λμμ μ΄λ»κ² νμλλ©° μ μ₯λλμ§λ₯Ό μ°κ΅¬νλ κ²μ μ°λ¦¬ κΈ°μ΅μ νμ±κ³Ό μ μ₯, μ¬μΈ κ³Όμ μ μ΄ν΄νλλ° μμ΄ λ§€μ° μ€μνλ€κ³ λ³Ό μ μλ€. λμμ μΌν κΈ°μ΅μ λ΄λΉνλ€κ³ μλ €μ§ μμμ ν΄λ§λ‘μ¨, ν΄λ§μ μμμ μ
μ νμλ€ λλ λλ¬Όλ€μ΄ κ³Όκ±°μ κΈ°μ΅μ μΈμΆνκ±°λ μλ‘μ΄ κΈ°μ΅μ νμ±νλλ° μμ΄ μ΄λ €μμ κ²ͺλλ€λ κ²μ΄ μ¬λ¬ μ€νμ ν΅ν΄ λ³΄κ³ λ λ° μλ€. ν΄λ§λ μΌν κΈ°μ΅λΏλ§ μλλΌ κ³΅κ°μ λν μ§λλ₯Ό νμ±νλ λ°μλ κ΄μ¬νλλ°, νΉν, μ€μΉλ₯ ν΄λ§μμ κ΄μ°° λλ μ₯μ μΈν¬κ° μ΄λ¬ν ν΄λ§μ κΈ°λ₯λ€μ μννλλ° ν΅μ¬μ μΈ μν μ νλ κ²μΌλ‘ μλ €μ Έ μλ€. νμ§λ§ μ₯μ μΈν¬λ μ£Όλ‘ μ₯κ° κ³΅κ°μ νμνλ κ³Όμ μμμ λ°ν ν¨ν΄μ κ΄μΈ‘ν μ°κ΅¬κ° μ£Όλ₯Ό μ΄λ£¨μμΌλ©° μ₯λ©΄ μκ·Ήμ΄ κ°λ³ μ₯μ μΈν¬μ λ°ν ν¨ν΄μ ν΅ν΄ μ΄λ»κ² νμμ΄ λλμ§μ λν μ°κ΅¬λ λ―Έλ―Έν μμ€μ΄λ€.
μ΄ λ
Όλ¬Έμμ λλ μ₯λ©΄ μκ·Ήμ΄ ν΄λ§μ μ₯μ μΈν¬μμ μ΄λ»κ² νμλλμ§λ₯Ό μμλ³΄κ³ μ μ₯κ° λͺ¨λν°μ μ μ λ μ₯λ©΄ μκ·Ήμ λ³΄κ³ μ€λ₯Έμͺ½μ΄λ μΌμͺ½μ μ νν΄μΌ νλ κ³Όμ λ₯Ό μν ν λ ν΄λ§μ νμ μμμΈ CA1κ³Ό ν΄λ§μ μ 보λ₯Ό μ λ¬ λ°μ λμ λ€λ₯Έ μμμΌλ‘ μ 보λ₯Ό μ λ¬νλ ν΄λ§μ΄νλΆμ λ¨μΌ μΈν¬ νλμ μΈ‘μ νμλ€. κ·Έ κ²°κ³Ό CA1κ³Ό ν΄λ§μ΄νλΆμμ κ΄μ°° λ μ₯μ μΈν¬λ€μ΄ μ₯λ©΄ μκ·Ήμ λ°λ₯Έ λ°νμ¨ λ³νλ₯Ό 보μΈλ€λ κ²μ νμΈ ν μ μμλ€. μ΄μ λνμ¬ λλ ν΄λ§μ μ₯μ μΈν¬λ€μ΄ μ₯μμ₯μ νμ±νκΈ° μν΄μ νμν μκ° μκ·Ήμ΄ λ¬΄μμ΄λ©°, μ΄μ μ₯λ©΄ μκ·Ήμ΄ μ΄λ€ μν μ νλμ§ νμΈνκΈ° μν΄ κ°μ νκ²½μ μ΄μ©ν μ€νμ μννμλ€. μ΄ μ€νμμλ μ₯κ° μ ν νΈλμ λ¬λ¦΄ λ, λΉ κ³΅κ°μμ μμνμ¬ μ₯λ©΄ μκ·Ήμ νμ± ν λκΉμ§ μκ° μκ·Ήμ νλμ© μΆκ°νλ©΄μ ν΄λ§μ νμ μμμΈ CA1κ³Ό CA3μ μ₯μ μΈν¬ νλμ μΈ‘μ νλ κ³Όμ μ ν΅ν΄ μ΄λ€ μκ° μκ·Ήμ΄ μ₯μ μΈν¬μ μ₯μμ₯ νμ±μ κ°μ₯ ν° μν₯μ λ―ΈμΉλ κ²μΈμ§ μμ보μλ€. κ·Έ κ²°κ³Ό CA1μ μ₯μ μΈν¬λ κ°λ¨ν μκ° μκ·Ήμ μΆκ°μλ μ₯μμ₯μ μ νμ±νλ λͺ¨μ΅μ λ³΄μΈ λ°λ©΄ CA3μ μ₯μ μΈν¬λ€μ μΆ©λΆν μκ° μκ·Ήμ΄ λͺ¨μ¬μ μ₯λ©΄ μκ·Ήμ νμ± ν κ²½μ°μ μ₯μμ₯μ νμ±νλ κ²μ΄ κ΄μ°°λμλ€. μ΄λ¬ν μΌλ ¨μ μ€νμ ν΅νμ¬ λλ μ₯λ©΄ μκ·Ήμ΄ ν΄λ§μ μ₯μ μΈν¬ λ°νλ₯Ό ν΅ν΄ νμλλ©°, ν΄λ§μ νμ μμμ΄ λͺ¨λ μ₯λ©΄ μκ·Ή μ²λ¦¬μ κ΄μ¬νμ§λ§ κ·Έ μ€μμλ νΉν CA3κ° μ₯λ©΄ μκ·Ήμ μ²λ¦¬ ν λμ ννμ¬ ν° νμ±μ 보μΈλ€λ κ²μ λ°νλ€.Abstract i
Table of Contents iii
List of Figures iv
Background 1
Scene processing in the hippocampus 2
Anatomical connections of CA1 and CA3 4
Properties of place cell activity 7
Chapter 1. Visual scene representation of CA1 and subiculum in the visual scene memory task 10
Introduction 11
Materials and methods 14
Results 31
Discussion 60
Chapter 2. Role of the visual scene stimulus for place field formation in CA1 and CA3 65
Introduction 66
Materials and methods 68
Results 80
Discussion 107
General Discussion 118
Bibliography 124
κ΅λ¬Έμ΄λ‘ 140Docto
Exploring Neighborhood Ranges through Walking Big Data: An Empirical Study based on WalkOn Data in Jamsil Area
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : 곡과λν 건μΆνκ³Ό, 2018. 8. μ΅μ¬ν.μνκΆμ μ£Όλ―Όλ€μ μΌμμν, μ¦ ν΅ν, ν΅κ·Ό, μΌν, μ€λ½ λ±μ΄ μ΄λ£¨μ΄μ§λ 곡κ°μ μμμΌλ‘, μμ μ΄κΈ° μ’μ λλ€λ₯Ό λ§λ€κ³ μ 건μΆβ
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μ΄λ€. μ°λ¦¬λλΌμ κ²½μ°, λκ·λͺ¨ κ°λ°λ‘ μ§νλ 곡λμ£Όν λ¨μ§ κ³νμ ν΅ν΄ μνκΆ κ³νμ΄ μ κ·Ήμ μΌλ‘ μλλμμΌλ©°, μ¬λ¬ μ°¨λ‘ λ¨μ§ κ³νμ κ±°μΉλ©° μνκΆ κ³ν κ°λ
μ΄ μ μ°¨ λ°μ λμλ€.
κ·Έλ¬λ μμ λ
μ κ±Έμ³ μ§νλ μλ§μ μνκΆ κ³ν μ¬λ‘κ° κ΅λ΄ λμ κ³³κ³³μ μμ§λ§, μ°λ¦¬κ° κ³νν λλ€κ° κ³Όμ° μνκΆμΌλ‘μ¨ μ μ ν κΈ°λ₯νκ³ μλμ§μ λν μ€μ¦μ κ²ν λ μμ§ λΆμ‘±ν μ€μ μ΄λ€. λ°λΌμ λ³Έ μ°κ΅¬λ μ μ€ κ±°μ£Όλ―Όμ μνμμμ μ€μ¦μ λ°μ΄ν°λ₯Ό ν΅ν΄ μΈ‘μ ν¨μΌλ‘μ¨, 70λ
λ λνμ μΈ μνκΆ κ³ν μ¬λ‘μΈ μ μ€ μ§κ΅¬μ μνκΆμ΄ νμ¬ μ΄λ»κ² κΈ°λ₯νλμ§λ₯Ό κ²ν νκ³ μ νμλ€.
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μ μ£Όλ―Όλ€μ μ΄λ ν¨ν΄μ κ΄λ²μν μκ°μ , 곡κ°μ λ²μμμ μμ§ν΄μΌνλ€. μ΄λ κΈ°μ‘΄μ μ΄λ ν¨ν΄ μμ§ λ°©μμΌλ‘λ νμ€μ μΌλ‘ μ΄λ €μ λ€. νμ§λ§ μ΅κ·Όμ νμ© κ°λ₯ν΄μ§ λͺ¨λ°μΌ λΉ
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WalkOn μ¬μ©μμ 보νκ²½λ‘ λ°μ΄ν°λ₯Ό κΈ°λ°μΌλ‘ μ μ€μ§μ κ±°μ£Όλ―Όμ μνμμμ μΈ‘μ νμλ€.
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μ°κ΅¬ λμμ§λ‘ μ μ€μ§κ΅¬μ `12κ° μ£Όκ±°μ§ λΈλ‘λ₯Ό μ μ νμμΌλ©°, ν΄λΉ λΈλ‘μ κ±°μ£Όλ―Όλ€μ μνμμ μΈ‘μ μ μν΄ 2016λ
9, 10, 11μμ WalkOn μ΄ν리μΌμ΄μ
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μ WalkOn μ μ μ 3κ°μκ° κ·Έλ€μ κ·Όλ¦° λ΄ λ³΄νκ²½λ‘ λ°μ΄ν° 31376κ°κ° λΆμμ μ¬μ©λμλ€. λ°μ΄ν° μ²λ¦¬ λ° μνμμ μΈ‘μ κ³Όμ μμ, WalkOn λ°μ΄ν°μ μ μ²λ¦¬ λ° λ³΄μ μ μν΄μλ Excel 2016λ₯Ό μ¬μ©νμμΌλ©°, Kernel Methodλ₯Ό ν΅ν μνμμ μΈ‘μ μ μν΄μλ ArcGIS 10.2.2μ 곡κ°λΆμ νλ‘κ·Έλ¨μΈ GME 0.7.3.0μ μ¬μ©νμλ€.
Kernel Methodλ₯Ό ν΅ν΄ μ΅μ’
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κ±°μ£Όλ―Όμ μνμμμ λΆμμ ν΅ν΄ λνλ μ£Όμ μ°κ΅¬ κ²°κ³Όλ λ€μκ³Ό κ°λ€.
첫째, μνμμμ μ ν λΆλ₯ κΈ°μ€μ μ μνμλ€. μ΄λμ±κ³Ό μκ°μ±μ κ³ λ €νμ¬ μνμμμ μ νμ λ€ κ°μ§λ‘ ꡬλΆν μ μμλ€. μ ν ꡬλΆμ κΈ°μ€μΌλ‘ μ΄λμ±μ κ±°μ£Όλ―Όμ΄ ν΄λΉ μνμμμ 보νμ ν΅ν΄ μ κ·Όνλμ§, 보ν μΈ κ΅ν΅μλ¨μ ν΅ν΄ μ κ·Όνλμ§λ₯Ό μλ―Ένλ©°, μκ°μ±μ κ±°μ£Όλ―Όμ΄ μΌλ°μ μΌλ‘ μνμ μμνλ 곡κ°(Kernel 90% μμ)μΈ μΌλ°μνκΆμΈμ§, κ±°μ£Όλ―Όμ μΌμμνμ΄ μ§μ€λλ 곡κ°(Kernel 50% μμ)μΈ μ§μ€μνκΆμΈμ§μ ꡬλΆμ μλ―Ένλ€. μ΄λ¬ν κΈ°μ€μ ν΅ν΄ 보ν μ κ·Ό μνμμμ΄λ©΄μ μΌλ°μνκΆμΈ κ·Όλ¦°μνμμ, 보ν μ κ·Ό μνμμμ΄λ©΄μ μ§μ€μνκΆμΈ κ·Όλ¦°μ§μ€μνμμ, 보ν μΈ κ΅ν΅μλ¨ μ κ·Ό μνμμμ΄λ©΄μ μΌλ°μνκΆμΈ μ§μμνμμ, 보ν μΈ κ΅ν΅μλ¨ μ κ·Ό μνμμμ΄λ©΄μ μ§μ€μνκΆμΈ μ§μμ§μ€μνμμμ 4κ°μ§ μ νμΌλ‘ μνμμμ ꡬλΆνμλ€.
λμ§Έ, μ μ€ κ±°μ£Όλ―Ό μνμμμ λ©΄μ κ³Ό μ΄μ κ΄λ ¨ν μμλ₯Ό λμΆνμλ€. λ¨Όμ μνμμμ λ©΄μ μ μ΄ν΄λ³΄λ©΄, κ·Όλ¦°μνμμ νκ· λ©΄μ μ 49.0ha, κ·Όλ¦°μ§μ€μνμμ νκ· λ©΄μ μ 9.1ha, μ§μμνμμμ νκ· λ©΄μ μ 5.0ha, μ§μμ§μ€μνμμμ λ©΄μ μ 1.5haλ‘ λνλ¬λ€. μ΄λ¬ν μνμμμ λ©΄μ λΆν¬λ₯Ό ν΅ν΄μ μΌλ°μ μΈ μ μ€μ§κ΅¬ κ±°μ£Όλ―Όλ€μ μνμμμ μ λμ μΌλ‘ νμ
ν μ μμλ€. μνμμμ λ©΄μ κ³Ό κ΄λ ¨μλ 첫 λ²μ§Έ μμλ κ±°μ£Όλ―Ό κ°μΈ νΉμ±μΈ μ°λ Ήλμ΄λ€. 40λ μ΄ν κ±°μ£Όλ―Όκ³Ό 50λ μ΄μ κ±°μ£Όλ―Όκ°μ μ°¨μ΄κ° λνλ¬λλ°, 40λ μ΄ν κ±°μ£Όλ―Όμ 50λ μ΄μ κ±°μ£Όλ―Όμ λΉν΄ κ·Όλ¦°μνμμ λ©΄μ μ΄ μκ³ , μ§μμνμμμ΄ ν¬κ² λνλ¬λ€. λ λ²μ§Έ μμλ μ§μμ νΉμ±μΈ κ±°μ£Όμ§μ μ€μ¬μ§κ΅¬ κ°μ 거리μ΄λ€. κ±°μ£Όμ§μ μ€μ¬μ§κ΅¬ κ° κ±°λ¦¬κ° κ°κΉμΈμλ‘ κ±°μ£Όλ―Ό κ·Όλ¦°μνμμμ λ©΄μ μ΄ μ»€μ§λ κ²½ν₯μ νμΈν μ μμλ€.
μ
μ§Έ, κ°μ λλ‘μ λμννμ κΈ°λ₯μ λνμ¬ κ³ μ°°νμλ€. κ·Όλ¦°μνμμμ ννλ₯Ό λΆμν κ²°κ³Ό, μνμμμ ννλ κ±°μ£ΌλΈλ‘ λ΄λΆμ λ©΄μ μΈ ννμ κ°μ λλ‘λ₯Ό ν΅ν΄ κ±°μ£ΌλΈλ‘ μΈλΆλ‘ νμ₯νλ μ μ μΈ ννλ₯Ό λͺ¨λ κ°λ κ²μ μ μ μμλ€. μ΄λ κ°μ λλ‘λΌλ λμννμ μμκ° μνκΆμ ꡬλΆνλ κ²½κ³λ‘μ κΈ°λ₯κ³Ό μνμμμ΄ νμ₯νλ ν΅λ‘λ‘μ κΈ°λ₯μ λͺ¨λ μ§λλ€λ κ²μ μλ―Ένλ€.
λ·μ§Έ, μ μ€μ§κ΅¬μ μννΈ λ¨μ§μ μμ‘±μ±κ³Ό νμμ±μ λνμ¬ κ³ μ°°νμλ€. λ¨Όμ μμ‘±μ±μ κ²½μ°, κ·Όλ¦°μνμμμ ννλ₯Ό λ΄λΆν, 근거리 νμ₯ν, μ거리 νμ₯νμΌλ‘ ꡬλΆνμ¬ λΆμν κ²°κ³Ό, μ μ€μ§κ΅¬μ μννΈ λ¨μ§ κ±°μ£Όλ―Όμ μνμμμ μ μΈ΅ μ£Όκ±°μ§ κ±°μ£Όλ―Όμ μνμμμ λΉνμ¬ λ΄λΆνμ΄ μ κ³ κ·Όκ±°λ¦¬ νμ₯νμ΄ λ§μ΄ λνλ, μννΈ λ¨μ§μ μμ‘±μ±μ΄ μ μΈ΅ μ£Όκ±°μ§μ λΉν΄ λ¨μ΄μ§μ μ μ μμλ€. κ·Έλ¦¬κ³ νμμ±μ κ²½μ°, κ·Όλ¦°μνμμ νμ₯ ν¨ν΄μ μ΄ν΄λ³΄μμ λ, μννΈ λ¨μ§κ° μΈλΆμΈλ€μ νμ₯ν μνμμμ ν¬ν¨λμ§ μλ κ²½ν₯μ΄ λνλ, μννΈ λ¨μ§κ° μΈλΆμΈλ€μκ² νμμ μΈ μ±κ²©μ μ§λλ€λ κ²μ νμΈν μ μμλ€. μ΄λ₯Ό μ’
ν©νλ©΄ μ μ€μ§κ΅¬μ μννΈ λ¨μ§λ μ μΈ΅ μ£Όκ±°μ§μ λΉνμ¬ μμ‘±μ μ΄μ§ λͺ»νλ©°, μΈλΆμΈλ€μκ² νμμ μΈ μμ±μ κ°λλ€κ³ ν μ μλ€.
μ§κΈκΉμ§ μ μ€μ§κ΅¬ κ±°μ£Όλ―Όμ μνμμμ κ°μΈ νΉμ± λ° λμννμ νΉμ±κ³Ό μ°κ΄μ§μ΄ κ³ μ°°ν΄λ³΄μλ€. λ³Έ μ°κ΅¬μ κ²°κ³Όλ κ±°μ£Όλ―Ό μνμμ μΈ‘μ μ μν΄ μλ‘μ΄ μλν μ°κ΅¬λ°©λ²λ‘ μ κΈ°λ°νλ€. λͺ¨λ°μΌ λΉ
λ°μ΄ν°λ₯Ό ν΅ν΄ μ»μ΄μ§ κ±°μ£Όλ―Ό 보ν νν λ°μ΄ν°λ₯Ό Kernel Methodλ₯Ό νμ©νμ¬ μνμμμ μΈ‘μ ν¨μΌλ‘μ¨ μνμμμ ꡬ체μ μ΄κ³ μ
체μ μΌλ‘ μΈ‘μ ν μ μμλ€. μ΄λ¬ν μλλ κΈ°μ‘΄μ μνμμ μΈ‘μ λ°©λ²μ νκ³λ₯Ό 극볡νκ³ , μνκΆ μ°κ΅¬μ λͺ¨λ°μΌ λΉ
λ°μ΄ν°λ₯Ό νμ©νλ λ°©λ²λ‘ μ μ μνλ€λλ° κ·Έ μμκ° μλ€.1. μ λ‘ 1
1.1 μ°κ΅¬μ λ°°κ²½κ³Ό λͺ©μ 1
1.2 μ°κ΅¬μ λ²μμ λ°©λ² 3
2. μ΄λ‘ μ κ³ μ°° 8
2.1 κ·Όλ¦° μνκΆ κ³νκ³Ό μ μ€μ§κ΅¬ 8
2.1.1 κ΅λ΄ μ£Όκ±°μ§ μνκΆ κ³νμ νλ¦ 8
2.1.2 μ μ€ μ§κ΅¬μ μνκΆ κ³νκ³Ό κΈ°μ‘΄ ν΄μ 10
2.2 맀νμ ν΅ν μνμμ μΈ‘μ 13
2.2.1 맀νμ λ°©λ²λ‘ μ κ°λ₯μ±κ³Ό νκ³ 13
2.2.2 맀νμ ν΅ν μ μ€μ μνμμ λΆμ 15
2.3 μμΉ λ°μ΄ν°λ₯Ό ν΅ν μνμμ μΈ‘μ 17
2.3.1 μ΄λκ²½λ‘ μΈ‘μ λ°©μμ νλ¦ 17
2.3.2 μμΉλ°μ΄ν°λ₯Ό ν΅ν μνμμ μΆμ μ°κ΅¬ 19
2.4 μ°κ΅¬μ μ°¨λ³μ± λ° νμμ± 23
3. λΆμμ ν 25
3.1 λμμ§ 25
3.1.1 λμμ§ λ²μ λ° κ°μ 25
3.1.2 λμμ§ λΆμ 27
3.2 WalkOn λ°μ΄ν° μμ± λ° νκ³ 31
3.2.1 λ°μ΄ν°μ μμ±κ³Ό νΉμ§ 31
3.2.2 λ°μ΄ν°μ νκ³ λ° λ³΄μ 33
3.3 λ°μ΄ν° μ λ³ λ° κ°μ 36
3.3.1 κ±°μ£Όλ―Ό μ λ³ κ³Όμ 36
3.3.2 λΈλ‘λ³ κ±°μ£Όλ―Ό μΈμ λ° νΉμ± 37
3.4 Kernel Methodλ₯Ό ν΅ν μνμμ μΆμ 40
3.4.1 컀λλ°λμΆμ (Kernel Density Estimation) 40
3.4.2 Kernel Methodλ₯Ό ν΅ν κ±°μ£Όλ―Όμ μνμμ μΆμ 42
4. μ μ€ κ±°μ£Όλ―Ό μνμμ μμ 46
4.1 μνμμ μ ν λ° μ νλ³ λΆν¬, λ©΄μ 46
4.1.1 μνμμ μ ν 46
4.1.2 μνμμ μ ν λ° λ©΄μ λΆν¬ 52
4.2 μνμμμ ννμ νΉμ± 60
4.2.1 κ·Όλ¦°μνμμ νν μ ν 60
4.2.2 κ·Όλ¦°μνμμ μ νλ³ νν νΉμ± 67
4.3 μμ€μ΄μ© μμ 74
4.3.1 κ·Όλ¦°μ§μ€μνμμ λ°νμ κ±°μ£Όλ―Ό μμ€μ΄μ© λΆμ 74
4.3.2 μ§μμ§μ€μνμμ λ°νμ κ±°μ£Όλ―Ό μμ€μ΄μ© λΆμ 81
4.4 λΆμκ²°κ³Ό μ’
ν© 83
4.4.1 μνμμ λ©΄μ λΆμ 83
4.4.2 μνμμμ νν λ° κ°μ λλ‘μ κΈ°λ₯ 85
4.4.3 λΈλ‘μ μμ‘±μ±κ³Ό νμμ± 86
5. κ²°λ‘ 90
μ°Έκ³ λ¬Έν 95
Abstract 98Maste
λ―Έλ€λ°μ€μ μ£μ§μ»΄ν¨ν μ μν μ μ‘ λ³΄μ κ³μΈ΅ νμ₯ μ°κ΅¬
νμλ
Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ»΄ν¨ν°κ³΅νλΆ, 2020. 8. κΆνκ²½.μΈν°λ· νΈλν½μ΄ HTTPSλ‘ μνΈνλλ©΄μ μΉ μΊμλ λ°©νλ²½ κ°μ λ―Έλ€λ°μ€λ νΉλ³ν μ‘°μΉκ° μμ΄λ λμνκΈ° μ΄λ €μ΄ μνκ° λμλ€. κ·Έλ¬λ€λ³΄λ νμ
μμλ λ―Έλ€λ°μ€λ₯Ό μνΈνλ μΈμ
μμ νμ©νκΈ° μν΄ κ³΅κ°ν€ μΈμ¦ ꡬ쑰μ μ λ’° λ°©μμ μ€μ©νμ¬ SplitTLSλΌκ³ νλ TLSλ₯Ό κ°λ‘μ±λ κΈ°λ²μ μ¬μ©νκ³ μλ€. κ·Έλ μ§λ§ μ§λ λͺ λ
κ° λ°νλ μ¬λ¬ λ
Όλ¬Έμμλ λ―Έλ€λ°μ€κ° μλͺ» ꡬνλμκ±°λ λ―Έλ€λ°μ€ μ€μ μ΄ μλͺ»λμ΄ SplitTLSλ₯Ό μννλλ° μμ΄ μ¬λ¬ 보μ λ¬Έμ κ° λ°μνκ³ μλ€λ κ²μ΄ λ°νμ‘λ€.
μ΄ λ
Όλ¬Έμ λ―Έλ€λ°μ€κ° TLS μΈμ
μ μμ νκ³ μ λ’°μ±μκ² μ°Έμ¬νκΈ° μν λ°©λ²μ μ€κ³νκ³ μ νλ€. μ΄λ₯Ό μν΄ μ°λ¦¬λ λ¨Όμ λ―Έλ€λ°μ€λ₯Ό μ€κ°μ μν μ μννλ λ―Έλ€λ°μ€μ μ’
λ¨μ μν μ μννλ λ―Έλ€λ°μ€λ‘ ꡬλΆνμλ€. μ€κ°μ μν μ μννλ λ―Έλ€λ°μ€λ ν΅μ μκ°μ μλ²μ ν΄λΌμ΄μΈνΈ κ°μ΄λ°μ λμμ μννλ μ€κ°μμ΄λ©°, μ’
λ¨μ μν μ μννλ λ―Έλ€λ°μ€λ μΈμ
μ΄ νμ±νλ λμ λ―Έλ€λ°μ€κ° μλ²μ²λΌ λμνλ κ°μ²΄λ₯Ό μλ―Ένλ€. μ μμ μλ‘λ μΉ¨μ
νμ§ μμ€ν
μ΄ μμΌλ©° νμμ μλ‘λ μΉ μΊμκ° μλ€. μ΄ κ΅¬λΆ νμμ μ°λ¦¬λ λ―Έλ€λ°μ€λ₯Ό TLS μΈμ
μ μ°Έμ¬μν€κΈ° μν 23κ°μ νλ‘ν μ½μ λν΄ κ²ν νμλ€.
23κ° μ€ 14κ°λ μ€κ°μ μν μ μννλ λ―Έλ€λ°μ€λ₯Ό μν νλ‘ν μ½μ΄λ©°, 9κ°λ μ’
λ¨μ μν μ μννλ λ―Έλ€λ°μ€λ₯Ό μν νλ‘ν μ½μ΄λ€.
μ°λ¦¬λ μ ν μ°κ΅¬λ₯Ό κ²ν νλ©΄μ λ€μμ κ΅νμ μ»μλ€. μ°μ μ€κ°μ μν μ μννλ λ―Έλ€λ°μ€λ₯Ό μν νλ‘ν μ½μ μ€κ³νλλ° μμ΄μ λ¨Όμ κ³ λ €ν΄μΌ ν μ μ, λ―Έλ€λ°μ€κ° κ³Όλν νΌλ―Έμ
μ κ°μ§ μλλ‘ μ΅μ κΆνμ μ€ μ μλ λ°©λ²μ μ°ΎμμΌ νλ€λ κ²μ΄μλ€. λν, μλ²κ° μΈμ
μ μ°Έμ¬νκΈ° λλ¬Έμ, μλ²κ° μνΈνμ λ°©λ²μ ν΅ν΄μ ν΄λΌμ΄μΈνΈμκ² λ―Έλ€λ°μ€μ λν
μ 보λ₯Ό μ€ μ μλ€λ μ λ κ³ λ €ν μ μλ€λ κ²μ μκ² λμλ€. λ€μμΌλ‘ μ’
λ¨μ μν μ μννλ λ―Έλ€λ°μ€λ₯Ό μν νλ‘ν μ½μ μ€κ³νλλ° μμ΄μ μ€μνκ² κ³ λ €ν΄μΌ ν μ μ μλ²κ° μΈμ
μ μ°Έμ¬νμ§ μκΈ° λλ¬Έμ, μλ²λ‘μ ν΅μ μ΄ μΆκ°λλ κ²μ λ°λμ§νμ§ μλ€λ μ κ³Ό ν€ κ΄λ¦¬μ μμ΄μ μλ²μκ² λΆνκ° κ°μ§ μλλ‘ λμ΄μΌ νλ©° κΈ°λ° ν€μ κ°μλ μ΅μνν μ μμ΄μΌ νλ€λ μ μ΄μλ€.
μ΄ λ
Όλ¬Έμμλ μ κ΅νμ μ λ°νμΌλ‘ maTLSμ TLS-SEEDλΌλ λ κ°μ νλ‘ν μ½μ μ μνμλ€.
λ¨Όμ , maTLS νλ‘ν μ½μ μ€κ°μ μν μ μννλ λ―Έλ€λ°μ€λ₯Ό μν νλ‘ν μ½μ΄λ€. νμ¬ λ―Έλ€λ°μ€λ₯Ό 보μ μΈμ
μ μ°Έμ¬μν€κΈ° μν SplitTLSλΌλ νλ‘ν μ½μ λ§€μ° λ§μ 보μ λ¬Έμ μ μ΄ λ°κ²¬ λμλ€. μ¬λ¬ μ ν μ°κ΅¬λ€μ΄ TLSμ λ―Έλ€λ°μ€λ₯Ό κ²°ν©νλ©΄μ μΈμ¦μ κ²μ¦ μ€ν¨λ μ€λλ μνΈ κΈ°λ²μ μ¬μ©νκ±°λ μμΉ μλ μμ μ νλ€λ κ²μ λ°ν λ΄μλ€. μ΄λ¬ν 보μ μ·¨μ½μ μ ν΄κ²°νκΈ° μν΄ μ°λ¦¬λ maTLS νλ‘ν μ½μ μ μνμλ€. μ΄ νλ‘ν μ½μ λ―Έλ€λ°μ€κ° TLS μΈμ
μ μμ μ λλ¬λ΄λ©΄μ κ°λ
λ μ μλ ννλ‘ μ°Έμ¬νλλ‘ νλ€. TLS μΈμ
μ μ°Έμ¬νλ λͺ¨λ λ―Έλ€λ°μ€λ€μ μΈμ
μ λ κ°μ μΈκ·Έλ¨ΌνΈλ‘ λΆν νλ©°
κ° μΈκ·Έλ¨ΌνΈλ ν΄λΉ μΈκ·Έλ¨ΌνΈλ₯Ό μν 보μ νλΌλ―Έν°λ₯Ό κ°λλ€. maTLS νλ‘ν μ½μ λ―Έλ€λ°μ€λ₯Ό μΈμ¦νκ³ κ° μΈκ·Έλ¨ΌνΈλ€μ 보μ νλΌλ―Έν°λ₯Ό κ²μ¦νλ©°, λ―Έλ€λ°μ€μ μ°κΈ° μ°μ°μ κ°λ
νλλ‘ μ€κ³λμλ€. μ΄λ κ² νμ¬ μ 체 μΈμ
μ 보μμ±μ΄ 보μ₯λλ€.μ΄ λ³΄μμ±μ΄ μ€μ λ¬μ±λλ€λ κ²μ 보μ΄κΈ° μν΄ μ°λ¦¬λ μ΅μ 보μμ± κ²μ¦ λκ΅¬μΈ Tamarinμ νμ©νμ¬ μ¦λͺ
νμμΌλ©° μ€μ ν
μ€νΈλ² λ μ€νμ ν΅ν΄ maTLSκ° μ½κ°μ μ€λ²ν€λλ₯Ό κ°μ§λ©΄μ μ 보μμ± λͺ©νλ₯Ό λ¬μ±νλ€λ κ²μ 보μλ€.
λ€μμΌλ‘ TLS-SEEDλ μ’
λ¨μ μν μ μννλ λ―Έλ€λ°μ€λ₯Ό μν νλ‘ν μ½μ΄λ€. νΉλ³ν μ°λ¦¬λ μ£μ§ μ»΄ν¨ν
μλ리μ€λ₯Ό κ³ λ €νλ©΄μ μ΄ νλ‘ν μ½μ μ€κ³νμλ€. μ£μ§ μ»΄ν¨ν
μ΄λ κ³μ°κ³Ό μ μ₯ λ
Έλλ₯Ό ν΄λΌμ΄μΈνΈμ κ°κΉκ² μμΉμμΌμ ν΄λΌμ΄μΈνΈμκ²λ λΉ λ₯Έ μλ΅μ μ 곡νκ³ μλ²μκ²λ λμν λΆνλ₯Ό μ€μ΄λλ‘ νλ€. μΌλ°μ μΌλ‘ μ£μ§ μ»΄ν¨ν
νλ«νΌμ μ ν리μΌμ΄μ
μ 곡μλ ν΄λΌμ΄μΈνΈμκ² μλ νν°μ΄κΈ° λλ¬Έμ μ΄ λ κ°μ²΄λ λͺ¨λ λμ μμ€μ 보μμ±μ μꡬν κ²μ΄λ€. μ΄μ λ°λΌ μ°λ¦¬λ \tlsλ₯Ό μ μνμμΌλ©°, μ΄λ₯Ό ν΅ν΄ μνν κ°μΈν€ 곡μ λ¬Έμ μ λΉν¨μ¨μ μΈ μ격 μ
μ¦ λ¬Έμ λ₯Ό ν΄κ²°νκ³ μ νμμΌλ©°, λμμ μ£μ§ μ»΄ν¨ν
μ μ₯μ μΈ μ±λ₯ ν₯μμ μ μ§ν λ‘ νκ³ μ νμλ€. TLS-SEEDλ μ ν리μΌμ΄μ
μλΉμ€ μ 곡μκ° i) μμ μ κ°μΈν€λ₯Ό 곡μ νμ§ μμΌλ©΄μλ μ£μ§ μ ν리μΌμ΄μ
μ λμ
ν μ μλλ‘ λ§λ€μ΄μ£Όκ³ , ii) μ격 μ
μ¦μ μννμ¬ μ£μ§ μ ν리μΌμ΄μ
μ μΈκ°νκ±°λ λΉμΈκ°ν μ μλλ‘ ν΄μ€λ€. λν μ ν리μΌμ΄μ
μλΉμ€ μ 곡μκ° ν΄λΌμ΄μΈνΈμκ² μ£μ§ μ ν리μΌμ΄μ
μ λν μΆ©λΆν μ 보λ₯Ό μ 곡νμ¬ ν΄λΌμ΄μΈνΈκ° μ
μ¦ μλΉμ€μ μμ‘΄νμ§ μλλΌλ μ£μ§ μ ν리μΌμ΄μ
μ λν΄ μ΄ν΄ν μ μλλ‘ ν΄μ€λ€. TLS-SEEDλ₯Ό μν ν΅μ¬ μλ£ κ΅¬μ‘°λ CrossCredential (CC)μ΄λ©°, μ΄λ ν΄λΌμ΄μΈνΈμκ² μ ν리μΌμ΄μ
μ 곡μμ μ λ’°ν μ μλ κΈ°κΈ° μ¬μ΄μ μ λ’° κ΄κ³λ₯Ό λͺ
μμ μΌλ‘ 보μ¬μ€λ€. CCλ λν ν΄λΌμ΄μΈνΈκ° μ£μ§ μ ν리μΌμ΄μ
μ 무결μ±μ κ²μ¦ν μ μλλ‘ μΆ©λΆν μ 보λ₯Ό μ 곡νλ€. TLS-SEEDλ₯Ό μνμ μΌλ‘ μ¦λͺ
νκΈ° μν΄, μ°λ¦¬λ ACCE-SEEDλΌλ TLS-SEEDλ₯Ό μν 보μ λͺ¨λΈμ λμ
νμλ€. μ΄ λͺ¨λΈμ TLSλ₯Ό μν ACCE λͺ¨λΈμ TLS-SEEDμ μ ν©νλλ‘ νμ₯ν κ²μ΄λ€. μ΄ λͺ¨λΈμ λ°νμΌλ‘ μ°λ¦¬λ TLS-SEEDκ° ACCE-SEED μμ νλ€λ κ²μ 보μλ€. λ§μ§λ§μΌλ‘, ν
μ€νΈ λ² λ κΈ°λ° μ€νμ ν΅ν΄ μ°λ¦¬λ TLS-SEEDκ° λ¬΄μν λ§ν λΆνλ§ μΌμΌν€κΈ° λλ¬Έμ μ€ν κ°λ₯νλ€λ κ²μ μ¦λͺ
νμλ€.Internet traffics are getting encrypted with HTTPS, which makes middleboxes blind. To introduce middleboxes in the encrypted session, the TLS interception schemes (\aka \splittls) that abuse the public key infrastructure (PKI) are widely used in practice. Several papers, however, demonstrate that the SplitTLS practice is risky due to incorrect implementation or misconfiguration of middleboxes.
This dissertation aims to design secure and trustworthy methods to introduce middleboxes in the TLS session. To this end, we first classify middleboxes into two types called a middlebox-as-a-middlebox and a middlebox-as-an-middlebox. A middlebox-as-a-middlebox is an intermediary between a client and a server at communication time, while a middlebox-as-an-endpoint is an intermediary that takes on the role of a server during the session. An example of the former is an intrusion detection system and that or the latter is a web cache. Then we conduct literature reviews over 23 protocols (14 protocols for a middlebox-as-a-middlebox and 9 protocols for a middlebox-as-an-endpoint) that make middleboxes participate into TLS sessions.
From our reviews, we have learned the following lessons. For a protocol with a middlebox-as-a-middlebox, we should consider the least privilege of a middlebox to limit it not to perform functionality with excessive permission in design. Also, since a server is involved into the session, we can use a server to help a client to understand a middlebox. For a protocol with a middlebox-as-an-endpoint, we should consider a method not to add further round-trips to a server. In addition, the number of secrets should be minimal and the overhead for the key management should not be placed on a server.
In this disseration, we propose two protocols called maTLS and TLS-SEED, based on our learnings.
The maTLS protocol is a protocol for a middlebox-as-a-middlebox. Existing solutions, such as SplitTLS, which intercepts TLS sessions, often introduce significant security risks by installing a custom root certificate or sharing a private key. Many studies have confirmed security vulnerabilities when combining TLS with middleboxes, which include certificate validation failures, use of obsolete ciphersuites, and unwanted content modification. To address these issues, we introduce a middlebox-aware TLS protocol, dubbed maTLS, which allows middleboxes to participate in the TLS session in a visible and auditable fashion. Every participating middlebox now splits a session into two segments with their own security parameters in collaboration with the two endpoints. The maTLS protocol is designed to authenticate the middleboxes to verify the security parameters of segments, and to audit the middleboxes' write operations. Thus, security of the session is ensured. We prove the security model of maTLS by using Tamarin, a state-of-the-art security verification tool. We also carry out testbed-based experiments to show that maTLS achieves the above security goals with marginal overhead.
The TLS-SEED protocol is a protocol for a middlebox-as-an-endpoint, especially considering a scenario of edge computing. Edge computing is an emerging technology to bring computation and data storage closer to clients, to provide fast responses and to reduce the bandwidth usage in cloud servers. An edge computing platform is typically a third party to an application service provider and a client, both of which require high security assurance. Therefore, we propose TLS-SEED, a TLS extension that addresses risky private key sharing and inefficient remote attestation on the third party, while preserving performance in edge computing. TLS-SEED allows an application service provider (i) to deploy its edge application without sharing its private keys, (ii) to authorize/deauthorize its edge application by performing remote attestation, while presenting sufficient information for a client to verify the edge application without relying on an attestation service. A central data structure of TLS-SEED is a Cross Credential (CC) that shows a client the trust relation between an application service provider and a trusted device. The CC also gives the client the ability to verify the integrity of the edge application. To formally analyze TLS-SEED, we introduce ACCE-SEED, a formal model for TLS-SEED, by extending the ACCE model for TLS, and show TLS is ACCE-SEED secure. Furthermore, testbed-based experiments show that TLS-SEED can be substantiated with a negligible performance overhead.Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Background 2
1.2.1 Types of Middleboxes 3
1.2.2 Transport Layer Security 4
1.2.3 X.509 Certificates 5
1.2.4 Certificate Transparency 5
1.2.5 TLS Interception 6
1.2.6 Problems of SplitTLS 7
Chapter 2 Literature Review 11
2.1 Middlebox-as-a-Middlebox 11
2.1.1 Types of Protocols 11
2.1.2 Takeaways 16
2.2 Middlebox-as-an-Endpoint 16
2.2.1 Types of Protocols 16
2.2.2 Takeaways 21
Chapter 3 maTLS: How to Make TLS middlebox-aware 22
3.1 Introduction 22
3.2 Trust and Threat Models 26
3.3 Auditable Middleboxes 27
3.3.1 Middlebox Certificates 27
3.3.2 Middlebox Transparency 28
3.3.3 Properties of Auditable Middleboxes 28
3.4 Middlebox-aware TLS (maTLS) 30
3.4.1 Security Goals 30
3.4.2 maTLS Design Overview 32
3.4.3 maTLS Handshake Protocol 38
3.4.4 maTLS Record Protocol 40
3.5 Security Verification 41
3.5.1 Protocol Rules 42
3.5.2 Adversarial Model 42
3.5.3 Security Claims 43
3.6 Evaluation 45
3.6.1 Experiment Settings 45
3.6.2 HTTPS Page Load Time 46
3.6.3 Scalability of Three Audit Mechanisms 48
3.6.4 CPU Processing Time 50
3.7 Discussions 51
3.7.1 Incremental Deployment 51
3.7.2 Abbreviated Handshake 51
3.7.3 Mutual Authentication 52
3.7.4 TLS 1.3 Compatibility 52
3.7.5 Mobility Support 52
3.7.6 P2P Communication 53
3.8 Conclusion 53
Chapter 4 TLS-SEED: How to SEcurely Communicate with EDge Computing Platforms 55
4.1 Introduction 55
4.2 Preliminary 58
4.2.1 Edge Computing 59
4.2.2 Trusted Execution Environment 61
4.2.3 TLS on the Third Party 63
4.3 SEED Overview 66
4.4 SEED Design 68
4.4.1 Security Goals 68
4.4.2 Cross Credential (CC) 69
4.4.3 TLS-SEED:TLS extensions for SEED 70
4.4.4 Implications of Cross Credential 75
4.5 Security Analysis 76
4.5.1 Overview of ACCE 76
4.5.2 ACCE-SEED Protocol Execution Environment 77
4.5.3 ACCE-SEED Security 80
4.5.4 Security Result 82
4.6 Evaluation 86
4.6.1 SEED Implementation 86
4.6.2 Experiment Settings 87
4.6.3 Performance Evaluation 88
4.7 Discussions 92
4.7.1 Incremental Deployment Scenario 92
4.7.2 Mobility Support 92
4.7.3 Dependency on TEEs 92
4.8 Conclusion 93
Chapter 5 Conclusion 94
Bibliography 96
Chapter A Cryptographic Definitions 105
A.1 Cryptographic Definitions 105
A.2 Oracles 109
κ΅λ¬Έμ΄λ‘ 110
Acknowledgements 113Docto
νλ‘μ€ν¬μΈ ꡬλ¨κ³Ό ν¬μ κ΄κ³μ μ§μ΄ μ¬λ¦¬μ μλ κ°μ λ―ΈμΉλ μν₯ :
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : 체μ‘κ΅μ‘κ³Ό, 2016. 8. μ΅μμ°½.νλμΈλ€μ΄ μ΄μκ°λλ° μμ΄μ ν볡μ μ€μν μμ μ€ νλμ΄λ€. μ°λ¦¬λ κ°μ‘±, μΉκ΅¬, λλ£μ κ°μ μ¬ν ꡬμ±μλ€κ³Ό ν¨κ» μ€ν¬μΈ λ₯Ό νκ³ , ν
λ λΉμ , μΈν°λ· λ±κ³Ό κ°μ 맀체λ₯Ό ν΅ν΄ μμ²νλ λ± λ€μν λ°©λ²μΌλ‘ μ€ν¬μΈ λ₯Ό ν΅ν΄ μΆμ μ¦κΈ°κ³ μλ€. νΉν νλ‘ μ€ν¬μΈ μ°μ
μ΄ λ°μ νλ©΄μ νΉμ ꡬλ¨μ μμνλ©° ν¬μΌλ‘μ μΌκ΅¬, μΆκ΅¬, λꡬ, 배ꡬ λ± λ€μν μ€ν¬μΈ λ₯Ό κ΄λνλ μΈκ΅¬κ° λκ³ μλ€. μμ λ€μ΄ μ’μνλ νμ μμνκ³ , κ΄λ ¨λ μ 보λ₯Ό μ¬νꡬμ±μκ³Ό 곡μ νλ νμμ νλμΈλ€μκ² μ¬κ° μνμ λμ΄ νλ μ¬νμ μλ‘μ΄ μ¬ν λ¬Ένλ‘ μ리μ‘μ κ²μ΄λ€. μ°λ¦¬κ° μ°κ³ μ§μ νμ μμνλ©° μμν μ§λ¨μ λν΄ κ°κ² λλ μ μ°©μ¬κ³Ό λμΌμλ ν¬μ ν볡과 κ΄κ³κ° μλ€. λ§μ μ νμ°κ΅¬λ€μμ νλ‘ μ€ν¬μΈ ν λμΌμκ° μ°κ³ μ§μ λμ μ£Όλ―Όκ³Ό ν¬μ ν볡μ κΈμ μ μΈ μν₯μ μ€λ€κ³ λ³΄κ³ νμλ€. νμ§λ§ μ€ν¬μΈ κ΄λκ³Ό μΈκ°μ μΆμ μ§μ λν ꡬ체μ μΈ κ΄κ³μ λν΄μλ μ°κ΅¬κ° λ―Έν‘ν μν©μ΄λ€.
μ΄λ¬ν λ§₯λ½μμ λ³Έ μ°κ΅¬μμλ κ΄κ³ λ§μΌν
μ΄λ‘ μ κ·Όκ±°νμ¬ κ΅¬λ¨κ³Ό ν¬μ κ΄κ³κ° ν¬μ ν볡μ μ΄λ ν μν₯μ μ£Όλμ§ μμλ³΄κ³ μ νλ€. ꡬ체μ μΌλ‘ νλ‘ μ€ν¬μΈ νμ κ΄κ³μ μ§μ 5κ°(μ§μ€μ±, νΈνμ±, λͺ°μ
μ±, μΉλ°κ°, μμμ°κ΄) νμ μμΈμ΄ ν¬μ μ¬λ¦¬μ μλ
κ°μ λ―ΈμΉλ μν₯μ κ²μ¦νκ³ μ νλ€. μ΄λ₯Ό ν΅ν΄ νμ μ μΌλ‘ κ΄λ μ€ν¬μΈ μ κ°μΈμ μΆμ μ§ κ΄λ ¨ μ°κ΅¬ λΆμΌμ μ¬μΈ΅μ μΈ λ°μ μ κΈ°μ¬νκ³ μνλ©°, μ€λ¬΄μ μΌλ‘λ μλ‘μ΄ κ΄κ³ λ§μΌν
ν¨λ¬λ€μ λ± μ€μ¦ μλ£λ₯Ό μ μνκ³ μ νλ κ²μ΄ λ³Έ μ°κ΅¬μ λͺ©μ μ΄λ€.
μ΄λ₯Ό μν΄ λ³Έ μ°κ΅¬μμλ νΈμνλ³Έ μΆμΆλ°©λ²μΌλ‘ μΆμΆν νκ΅ νλ‘μΌκ΅¬ ν¬μ λμμΌλ‘ μ¨λΌμΈ μ€λ¬Έμ‘°μ¬λ₯Ό μννμμΌλ©°, μ΄ 300λΆμ μλ£λ₯Ό μμ§νμλ€. μ΄ μ€ 45κ°μ λΆμ±μ€ν μλ΅μ μ μΈν 285λΆμ μλ£λ₯Ό ν΅κ³ λΆμμ μ¬μ©νμλ€. μ°κ΅¬κ°μ€μ κ²μ¦νκΈ° μνμ¬ SPSS 23μ μ¬μ©νμ¬ κΈ°μ ν΅κ³λΆμ, μ λ’°λλΆμ, λ€μ€ νκ·λΆμμ μννμμΌλ©°, AMOS 2.0μ μ¬μ©νμ¬ νμΈμ μμΈλΆμμ μννμλ€.
μ°κ΅¬ κ²°κ³Ό, νλ‘ μ€ν¬μΈ νκ³Ό ν¬ κ°μ νΈνμ±κ³Ό μΉλ°κ°μ ν¬μ μ¬λ¦¬μ μλ
κ°μ ν΅κ³μ μΌλ‘ μ (+)μ μν₯μ λ―ΈμΉλ κ²μ νμΈνμλ€. λ°λ©΄, νλ‘ μ€ν¬μΈ νκ³Ό ν¬ κ°μ μ λ’°μ±, λͺ°μ
μ±, μμμ°κ΄μ ν¬μ μ¬λ¦¬μ μλ
κ°μ ν΅κ³μ μΌλ‘ μ μλ―Έν κ΄κ³κ° μλ κ²μΌλ‘ λνλ¬λ€. λ§μ§λ§μΌλ‘ μνλ μ‘°μ ν¨κ³ΌλΆμμμλ κ²½κΈ°μ₯ λ΄ μλΉμ€ μ§μ΄ ν¬μ μ¬λ¦¬μ μλ
κ°μ κ΄κ³μ λ―ΈμΉλ μν₯μ΄ μλ κ²μΌλ‘ λνλ¬λ€.For human beings, happiness or well-being is one of the most important elements in their life. For many, good quality of life is the ultimate goal of human life. People everywhere enjoy sports through participating and watching at home or stadiums with their social members such as friends and family. Through involvement in sports, people can attain an enjoyable life and happiness. Previous studies have proved that participating in physical and leisure activities has a positive effect on human happiness or well-being. Moreover, spectator sports have a positive influence on human happiness. For example, self-identification as a fan for a particular team is a factor that affects a fans psychological well-being. However, there is not much existing research indicating which specific factors in spectator sports affect quality of life.
This study applied relationship marketing theory to professional sports context and linked it with human psychological well-being. The purpose was to examine the influence of relationship quality between professional sports teams and fans on the fans psychological well-being in order to find how much the 5 factors of relationship quality impact the fans psychological well-being, and then to show how the findings from this research can lead to practical developments in both practical and academic sectors.
This study conducted an online survey and collected 330 completed survey samples from professional baseball fans in Korea. From the 330 collectged samples, 285 samples were ultimately used for data analysis. SPSS 21.0 was applied for descriptive, reliability, and multiple regression analysis. Confirmatory factor analysis was performed through using AMOS 20.0.
The result of this study found that reciprocity and intimacy between a sports team and its fan has a positive effect on the fans psychological well-being, even though there are no positive effects in trust, commitment, and self-connection. In addition, service quality was measured as a moderator variable and proved that there is no moderated effect on or between two variables: relationship quality and psychological well-being.
The findings of this study determined that relationship marketing in professional sports context can enhance peoples psychological well-being. With this theoretical evidence, sports managers can better understand the importance of relationship marketing regarding consumers happiness. The expected potential result is the creation of better effective marketing strategies.Chapter 1. Introduction 1
1.1 Background 1
1.2 Research Objective 6
1.3 Research Questions 7
1.4 Definition of Terminology 7
1.4.1 Relationship Quality 7
1.4.2 Psychological Well-Being 7
1.4.3 Service Quality 7
Chapter 2. Literature Review 8
2.1 Social Identity Theory 8
2.1.1 Influence of Social Identity Theory on Human Well-Being 10
2.2 Relationship Marketing 13
2.3 Relationship Quality 18
2.3.1 Trust 23
2.3.2 Commitment 26
2.3.3 Intimacy 27
2.3.4 Self-Connection 29
2.3.5 Reciprocity 31
2.4 Psychological Well-being 33
2.5 Research Model & Hypothesis 39
2.5.1 Research Model 39
2.5.2 Hypothesis 40
Chapter 3. Method 49
3.1 Participants and Procedures 49
3.2 Instrumentation 50
3.2.1 Item Development 50
3.2.2 Independent Variable 51
3.2.3 Dependent Variable 52
3.2.4 Moderator Variable 52
3.2.5 Control Variable 52
3.2.6 Pilot Study 53
3.3 Measurement 54
3.3.1 Descriptive Analysis 54
3.3.2 Confirmatory Factor Analysis 54
3.3.3 Reliability Analysis 55
3.3.4 Multiple Regression Analysis 55
Chapter 4. Results 56
4.1 Descriptive Statistics 56
4.2 Confirmatory Factor Analysis 58
4.3 Reliability Analysis 61
4.4 Hypothesis Verification 71
Chapter 5. Discussion & conclusion 78
5.1 Discussion 78
5.2 Implication 80
5.2.1 Theoretical Implication 80
5.2.2 Managerial Implication 82
5.3 Limitation & Future Research 83
5.4 Conclusion 85
REFERENCES 86
κ΅λ¬Έ μ΄λ‘ 95
APPENDIX 98Maste
A Constitutional Study on the Current Law Regulating Child Pornography in Korea
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : λ²νκ³Ό, 2015. 2. μ‘μμ€.νλ² μ 21μ‘°κ° λ³΄μ₯νλ ννμ μμ λ λ―Όμ£Όμ¬ν λ°μ μ νμλΆκ°κ²°ν κΈ°λ³ΈκΆμΌλ‘μ μ°λ¦¬λλΌλ₯Ό λΉλ‘―ν μΈκ³ κ°κ΅μμ λ§€μ° μ€μνκ² λ€λ£¨κ³ μλ κ°μΉλ€. ννμ μμ μ μνμ¬ λ³΄νΈλλ ννμ λ°λμ λμ μ μΉμ Β·μμ μ κ°μΉ λ±μ μ§λ
μΌ νλ κ²μ μλλ©° μΈκ°μ μ±(ζ§)μ λ€λ£¬ ννλ¬Ό μμ ꡬ체μ μΈ λ³΄νΈ λ²μμ λν λ
Όλμ μ‘΄μ¬ν μ μλλΌλ μ΅μν μΌμ λ²μ μ΄μμ ννμ μμ μ μνμ¬ λ³΄νΈλ μ μλ€. νν μλΒ·μ²μλ
μ μ±λ³΄νΈμ κ΄ν λ²λ₯ μ μλμ κΆμ΅ 보μ₯ λ° μλμ λν μ±λ²μ£ μλ°©μ΄λΌλ κΈ°μΉ νμ μλμ μ±(ζ§)μ κ΄νμ¬ λ€λ£¬ ννλ¬Ό, μ¦ μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ κ·μ λ₯Ό κ°ννλ λ°©ν₯μΌλ‘μ κ°μ μ΄ μ§μλκ³ μλ€. μ΄μ λνμ¬ λ
Όλμ΄ κ°μ΄λκ³ μλ νμ¬ μμ μμ ννλ²μ΄ ννμ μμ λ₯Ό λΉλ‘―ν κΈ°λ³ΈκΆ μ νμ κ΄μ μμ κ³ μ°°ν λ νλ²μ νμ©λ μ μλ νκ³λ₯Ό μ€μνκ³ μλ κ²μΈμ§ νλ²μ―€ κ²ν νμ¬ λ³Ό νμκ° μλ€.
μ΄ λ
Όλ¬Έμ μ±(ζ§) ννκ³Ό ννμ μμ μ 보νΈμμμ κ΄ν λ
Όμλ₯Ό μ κ²°μ μΌλ‘ κ²ν ν ν μ΄λ¬ν νλ²μ μ°¨μμμμ κ³ μ°°μ μ μ λ‘ νμ¬ ννλ²μμ μλ ν¬λ₯΄λ
Έκ·ΈλνΌ κ΄λ ¨ κ·μ μ μνμ± μ¬λΆλ₯Ό κ³ μ°°νλ€. λ¨Όμ , ννλ²μ μ± ννμ μ νκ³Ό κ΄λ ¨νμ¬ λ¬Ένμ Β·μ¬ννμ κ°λ
μΈ ν¬λ₯΄λ
Έκ·ΈλνΌλΌλ μ©μ΄ λμ μ μΌλ₯ μ μΌλ‘ μλμ΄λΌλ μ©μ΄λ₯Ό μ¬μ©νκ³ μλ€. ννμ μμ μ νλλΌλ κ΄μ μμ μ΅μν μ¨λΌμΈ 곡κ°μμμ ννμ κ·μ¨νλ μ 보ν΅μ λ§ μ΄μ©μ΄μ§ λ° μ λ³΄λ³΄νΈ λ±μ κ΄ν λ²λ₯ μμ μλμ λ²μλ νλ²μ¬νμκ° νμν λ°μ²λΌ μΆμ ν΄μν¨μ΄ νλΉνλ€. λν λ―Έμ°λ°©λλ²μμ κ²½μ°μλ λ¬λ¦¬, μλ ννμ΄λΌλ μΌλ¨ νλ² μ 21μ‘°μ ννμ μμ μ 보νΈμμμ ν΄λΉνλ€κ³ λ³΄κ³ μ΄λ₯Ό νλ² μ 37μ‘° μ 2νμ μνμ¬ μ νν μ μμ λΏμ΄λΌκ³ νμν μ°λ¦¬ νλ²μ¬νμμ νλλ νλΉνλ€. μ΄λ¬ν λ
Όλ¦¬λ μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ κ²½μ°μλ λ§μ°¬κ°μ§λ‘ μ μ©λ μ μμΌλ, 18μΈ λ―Έλ§μ μ€μ μλμΌλ‘ νμ¬κΈ μ€μ μ νΉμ κ°μμ μ±νμλ₯Ό νλλ‘ νλ μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ κ²½μ°μλ λΉν΄ μλμκ² λ―ΈμΉλ μΈκ²©μ μΉ¨ν΄κ° λ§€μ° μ€λνλ―λ‘ μμΈμ μΌλ‘ νλ²μ ννμ μμ μ 보νΈμμμμ λ°°μ νμ¬μΌ ν κ²μ΄λ€.
μ΄λ¬ν λ
Όμλ€μ λ°νμΌλ‘ νμ¬ λ³Έκ²©μ μΌλ‘ μλΒ·μ²μλ
μ μ±λ³΄νΈμ κ΄ν λ²λ₯ μ μλ ν¬λ₯΄λ
Έκ·ΈλνΌλ₯Ό κ·μ νκ³ μλ μ‘°νλ€μ μνμ±μ κ²ν νμ¬ λ³Έλ€. λ¨Όμ , κ΅λ΄μ λ―Έμ±λ
μ κ΄λ ¨ λ²μ²΄κ³ μ λ°κ³Ό νλ²μ 보νΈμμκ³Όμ κ΄κ³ λ±μ μ’
ν©μ μΌλ‘ κ³ μ°°ν λ μλΒ·μ²μλ
μ μ±λ³΄νΈμ κ΄ν λ²λ₯ μμ μ¬μ©νκ³ μλ κ°λ
μΈ μλΒ·μ²μλ
μ΄μ©μλλ¬Όμ κ΄ν μ°λ Ήμ 18μΈλ‘ νν₯ μ‘°μ ν νμκ° μλ€. λν μλΒ·μ²μλ
μ΄μ©μλλ¬Ό μ 2μ‘° μ 5νΈμ μ μ μ‘°ν μ€ μλΒ·μ²μλ
μΌλ‘ λͺ
λ°±νκ² μΈμλ μ μλ μ¬λ λΆλΆ λ° ννλ¬Ό λΆλΆ μμ ννμ μμ λ₯Ό μΉ¨ν΄νλ κ³Όλν κ·μ μ΄λ―λ‘ μμ κ° μμ²λλ€. λΏλ§ μλλΌ μλΒ·μ²μλ
μ΄μ©μλλ¬Όμ λ¨μ μμ§λ₯Ό μ²λ²νλ λ λ² μ 11μ‘° μ 5ν μμ κ³ΌμκΈμ§μμΉμ μλ°νμ¬ κ΅λ―Όμ κΈ°λ³ΈκΆμ μΉ¨ν΄νλ―λ‘ μμ κ° μꡬλλ€. μλ μ±λ²μ£ μλ°©μ΄λΌλ 곡μ΅λ μμ€νμ§λ§ μ΄λ¬ν κ°μΉλ₯Ό μΆκ΅¬νλ κ³Όμ μμ ννμ μμ λ₯Ό λΉλ‘―ν νλ²μ κΈ°λ³ΈκΆμ 보μ₯νμ¬μΌ νλ€λ μμ²κ³Όλ μ‘°νλ₯Ό μ΄λ£¨λλ‘ νμ¬μΌ νλ€. μ΄λ₯Ό μνμ¬ νν μλΒ·μ²μλ
μ μ±λ³΄νΈμ κ΄ν λ²λ₯ μ κ°λ²μ±μ μΆμνλ λ°©ν₯μΌλ‘μ κ°μ μ΄ νμνλ€.μ 1 μ₯ μλ‘ 1
μ 1 μ μ°κ΅¬μ λͺ©μ 1
μ 2 μ μ°κ΅¬μ λ°©λ²κ³Ό λ²μ 3
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1. μ°λ¦¬λλΌμ κ²½μ° 5
κ°. ννμ μμ μ μμ λ° λ΄μ© 5
λ. μΈλ‘ β€μΆνμ μμ μ 보νΈμμκ³Ό μ ν 6
2. λ―Έκ΅μ κ²½μ° β μμ νλ² μ 1μ‘°μ νλ 7
μ 2 μ ν¬λ₯΄λ
Έκ·ΈλνΌμ μλλ¬Όμ μμ 9
1. ν¬λ₯΄λ
Έκ·ΈλνΌμ μ μ λ° λΆλ₯ 9
2. μλ κ°λ
μ κ΄ν κ³ μ°° 12
κ°. ννλ²μμ μλ κ°λ
12
λ. λλ²μ νλ‘μ λν₯ 14
λ€. νλ²μ¬νμ νλ‘μ νλ 15
λΌ. κ²ν β μ 보ν΅μ λ§ μ΄μ©μ΄μ§ λ° μ λ³΄λ³΄νΈ λ±μ κ΄ν λ²λ₯ μμ μλ κ°λ
μ μ€μ¬μΌλ‘ 17
3. μκ²° 21
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1. νλ²μ¬νμμ νλ 22
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λ. λ³κ²½ ν 2006νλ°109 κ²°μ μ μμ§ 22
λ€. νλ‘ λ³κ²½μ λν μ°¬λ°λ‘ 24
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κ°. Miller v. California νκ²°μ μμ§ 26
λ. νκ²°μ νμ 26
3. κ²ν 28
μ 4 μ μκ²° 33
μ 3 μ₯ μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ ννμ μμ μ 보νΈμμμμ ν΄λΉ μ¬λΆ 34
μ 1 μ μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ μμ λ° ν΄μ
34
1. μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ μμ 34
2. μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ ν΄μ
36
κ°. μλμ λν μ±μ νλμ μΌκΈ° 36
λ. μλ μ±λ²μ£μ μ λ° λ° λ°©μ‘° 38
3. μκ²° 40
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1. νλ²μ¬νμ νλ‘μ νλ 40
κ°. νμμ¬ν 40
λ. λμ νλ‘μ νμ 41
2. λ―Έκ΅ νλ‘μ νλ β New York v. Ferber νκ²°μ μ€μ¬μΌλ‘ 43
κ°. μ¬μ€κ΄κ³ 43
λ. νμμ¬ν 43
3. μ°λ¦¬ νλ²μ μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ ννμ μμ μ μν λ³΄νΈ κ°λ₯μ± μ¬λΆμ λ
Όκ±° 44
4. νλ²μ ννμ μμ μ 보νΈμμμμ λ°°μ λλ μλ ν¬λ₯΄λ
Έκ·ΈλνΌμ λ²μμ κ΄ν κ³ μ°° 48
κ°. μ€μ μλμΈμ§ μ¬λΆμ λ°λΌ ννμ μμ μ 보νΈμμμμ ν΄λΉ μ¬λΆκ° λ¬λΌμ§λμ§ μ¬λΆ 48
λ. μλμ λ²μ μ€μ λ¬Έμ 49
λ€. λ¬μ¬μ λ
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μ 3 μ μκ²° 54
μ 4 μ₯ ννλ²μ μλμλλ¬Ό κ·μ μ κ΄ν μμ 55
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2. μλμλλ¬Ό κ΄λ ¨ κ·μ μ κ°μ μ°ν 58
3. ννλ²μ νλ²μ κ΄μ μμμ κ³ μ°° 60
μ 2 μ λμ(η«₯ι‘) μ±μΈ λ±μ₯ ννλ¬Όμ κ΄ν μμ 62
1. μλβ€μ²μλ
μΌλ‘ λͺ
λ°±νκ² μΈμλ μ μλ μ¬λ λΆλΆμ ν΄μμ κ΄ν νλ‘μ νλ 62
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3. μκ²° 71
μ 3 μ κ°μ μλ(virtual child) ν¬λ₯΄λ
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λ€. νλ²μ¬νμ νλ‘μ νλ 75
3. μΈκ΅μ μ¬λ‘ 76
κ°. κ΅μ νμ½μ νλ 76
λ. λ―Έκ΅μ νλβ Ashcroft v. Free Speech Coalition νκ²°μ μ€μ¬μΌλ‘ 78
λ€. μΌλ³Έμ νλ 80
λΌ. κ²ν 80
4. κ°μ μλ ν¬λ₯΄λ
Έκ·ΈλνΌ μ²λ²μ μνμ± 81
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λ. κ³ΌμκΈμ§μμΉμ μλ° μ¬λΆ 82
5. μκ²° 85
μ 4 μ μλμλλ¬Ό λ¨μ μμ§μ μ²λ² κ·μ μ κ΄ν μμ 86
1. μ²λ² λμμΈ μλμλλ¬Ό λ¨μ μμ§μ μμ 86
κ°. μ€μ λ²μ νλ 86
λ. κ²½μ°°μ²μ μλμλλ¬Ό μμ§μ λ¨μ κΈ°μ€ 86
λ€. μμ§μ κ°λ
μ λν κ³ μ°° 88
2. μλμλλ¬Όμ λ¨μ μμ§λ₯Ό μ²λ²νλ κ²μ μνμ±μ κ³ μ°° 89
κ°. μλμλλ¬Ό λ¨μ μμ§ μ²λ²μ λν 견ν΄μ λ립 89
λ. κ²ν β κ³ΌμκΈμ§μμΉμ κ΄μ μ μ€μ¬μΌλ‘ 91
3. μκ²° 96
μ 5 μ μκ²° 97
μ 5 μ₯ κ²°λ‘ 99
μ°Έκ³ λ¬Έν 103
Abstract 107Maste
On the Semantic Oppositions in The Bronze Horseman
There has been many reputes on the theme of The Bronze Horseman(1833) which is one of the finest works of A. Pushkin, but the riddle of the discrepancy between the image of Peter the Great in introduction and that of the Bronze Horseman of the main story about a 'little man' named Evgeny who lost his fiancee by flood has not been solved. According to A. Blok, Pushkin was a poet of harmony and he always tried to find middle way in conflicting situation in his time through his writings. As a poet he recognize the importance and the greatness of Peter the Great who built the Petersburg, but he could not reconcile with the surplus-repression of Nicholas who was a descendent of Peter and a personal censor of him. So the ambiguous attitude of Pushkin to Peter results in the dual oppositions in The Bronze Horseman, which has made readers embarrass in finding the real or secret message of this work. This paper argues that the real opposition on the semantic and thematic level in the work lies not in the opposition between Peter and Evgeny, but in the opposition between Peter and the Bronze Horseman implying Nicholas. That was a technique for Pushkin to satisfy the conflicting demands from inside and outside
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