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Shelter from the Holocaust. Rethinking Jewish Survival in the Soviet Union
No full textBecause publisher policy
Veri madenciliği yaklaşımı ile sosyal ağ analizi
No full textGünümüzde internet kullanmnn yaygnla³masyla geli³tirilen uygulamalar hem ileti³im hem de e§lence amaçl olarak ortaya çkm³tr. Sosyal a§lar olarak adlandrlan bu uygulamalar ki³iler, toplumlar hakknda büyük miktarda veriye internet üzerinden kolay ³ekilde eri³im imkan sunmaktadr. Sosyal a§lar üzerinde yaplan veri madencili§i çal³malar ise son yllarda bu alandaki geli³meler ile art³ göstermi³tir. Pek çok ara³trmann konusu olarak geni³ kitleler hakknda yararl bilgiler elde edilmeye çal³lm³tr. Bu tez çal³masnda sosyal a§larda yaplan veri madencili§i çal³malar ve problemleri ara³trlm³tr. Twitter uygulamas üzerinden verilere eri³ilerek Türkçe Tweetlerin duygu analizi yaplm³tr. Duygu snandrma i³lemi için Naive Bayes, Destek Vektör Makineleri ve K en yakn kom³u algoritmalar kullanlm³tr. Twitter kullanclarnn belirlenen sektördeki kurumsal ³irketler ile ilgili tweetleri duygu polaritesi açsndan incelenerek sosyal a§lar üzerinde kurumsal itibar en yüksek kurulu³ tespit edilmeye çal³lm³tr.azarlk Beyan iii
Öz iv
Te³ekkür vi
ekil Listesi x
Tablo Listesi xi
Ksaltmalar xii
1 Giri³ 1
2 Veri Madencili§i 3 2.1 Veri Madencili§i Nedir? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Veri Madencili§i Geli³im Süreci . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 Veri Madencili§i Modelleri . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3.1 Tanmlayc Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3.2 Tahmin Edici Model . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Veri Madencili§i A³amalar 8 3.1 Problemi Anlama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.1 Prol Analizi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.2 Segmentasyon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.1.3 Yant Modeli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.4 Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.5 Aktivasyon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.6 Çapraz Sat³ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.7 Ypranma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.8 Net Bugünkü De§er . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.9 Ömür Boyu De§er . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 Veriyi Anlama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Modelleme için Veri Seçme . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.4 Modelleme Metodolojisini Seçme . . . . . . . . . . . . . . . . . . . . . . . 15 3.5 Veri Hazrlama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.5.1 Örnekleme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.5.2 Veri Kalitesinin Sürdürülmesi . . . . . . . . . . . . . . . . . . . . . 16 3.5.3 Aykr De§er Analizi . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.5.4 Kayp De§er . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.6 De§i³kenlerin Seçimi ve Dönü³türülmesi . . . . . . . . . . . . . . . . . . . 18 3.7 Modelin Uygulanmas ve De§erlendirilmesi . . . . . . . . . . . . . . . . . . 18 3.8 Modelin Kullanlmas ve zlenmesi . . . . . . . . . . . . . . . . . . . . . . 19 3.8.1 De§erleme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.8.2 Yaynlama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 Veri Madencili§i ³levleri 21 4.1 Karakterizasyon ve Ayrt Etme . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2 Birliktelik Kural . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3 Snandrma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.4 Tahmin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.5 Kümeleme Analizi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.6 Aykr Veri Analizi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.7 De§i³im Analizi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.8 Görselle³tirme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5 Veri Madencili§i Algoritmalar 26 5.1 Karar A§açlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5.2 Genetik Algoritmalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5.3 Sinir A§lar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 5.4 statistik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6 Veri Madencili§i Uygulama Alanlar 32 6.1 Bilimsel ve Mühendislik Verileri . . . . . . . . . . . . . . . . . . . . . . . . 32 6.2 Sa§lk Verileri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.3 ³ Verileri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 6.4 Al³veri³ Verileri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.5 Bankaclk ve Finans Verileri . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.6 E§itim Alan Verileri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.7 nternet Verileri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6.8 Doküman Verileri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 6.9 Askeri Veriler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 6.10 Sosyal A§ Verileri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
7 Sosyal A§lar 35 7.1 Çizge Teorisi Yakla³m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 7.2 Sosyal A§larn Genel Özellikleri . . . . . . . . . . . . . . . . . . . . . . . . 36 7.3 Sosyal A§ Uygulamalarnda leti³im . . . . . . . . . . . . . . . . . . . . . 37 7.4 Sosyal A§ Uygulamalar . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
8 Sosyal A§larda Veri Madencili§i 38 8.1 Web Madencili§i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 8.2 Sosyal A§larda Web Madencili§i . . . . . . . . . . . . . . . . . . . . . . . 39 8.2.1 Kaynak Bulma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 8.2.2 Bilgi Çkarm ve Ön ³leme . . . . . . . . . . . . . . . . . . . . . . 40 8.2.3 Genelle³tirme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 8.2.4 Çözümleme (Analiz) . . . . . . . . . . . . . . . . . . . . . . . . . . 40
8.3 Web Madencili§i Yöntemleri . . . . . . . . . . . . . . . . . . . . . . . . . . 40 8.3.1 Web çerik Madencili§i . . . . . . . . . . . . . . . . . . . . . . . . . 41 8.3.2 Web Yap Madencili§i . . . . . . . . . . . . . . . . . . . . . . . . . 41 8.3.3 Web Kullanm Madencili§i . . . . . . . . . . . . . . . . . . . . . . . 41 8.4 Fikir Madencili§i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 8.5 lgili Çal³malar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
9 Uygulama 51 9.1 Veri Seti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 9.2 Uygulamada Kullanlan Program . . . . . . . . . . . . . . . . . . . . . . . 52 9.3 Yaplan Çal³ma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 9.4 De§erlendirme Ölçütleri . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 9.4.1 Do§ruluk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 9.4.2 Hassasiyet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 9.4.3 Kesinlik . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 9.4.4 Hatal Pozitif Oran . . . . . . . . . . . . . . . . . . . . . . . . . . 69 9.4.5 E§ri Alt Alan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 9.5 Analiz Sonuçlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
10 Sonuç 80
A Java Kodu 81
B Mutluluk/Üzgünlük Bildiren Karakter ve Kelimeler 83
Kaynaklar 8
[Kemal Karpat'a ait fotoğraf]
No full textKemal Karpat Arşivi. Not: Kemal Karpat'ın yanındaki kişinin kim olduğu bilinmemektedir
Reconsidering the meaning of topography (via the city of Istanbul)
Full text linkMimarlık kuramının, doğa ile olan hesaplaşması bugünlerde topoğrafya ile zorlu bir süreçte. Oysa topoğrafya, mimarlığın ontolojik bir
parçası olarak, doğanın biçim dilinden öte anlamlara açılan bir ara yüzdür; edilgen bir veri olmayıp, yerin ruhunu içinde barındıran, mekânı
örgütleyen, örtük bir yön vericidir; hem insan eylemlerine hem de mimarlığa dair bir şeyler fısıldar. Bu fısıltıya kulak verildiği ölçüde
doğal çevre ile yapılı çevre arasında bir diyalog, mekân ve toplum arasında da bir anlam inşa edilebilir. Bu noktadan hareket edildiğinde, topoğrafyanın, mimarlık literatüründe yerleşik olan kullanımından çok daha derin bir karşılığı olduğu görülür. Ancak, moderniteyle
birlikte topoğrafyanın da tasarım süreciyle yolları ayrışmış, mimarlıkta anlam arayışı çeşitli alanlara dağılmıştır. Üstelik son zamanlardaki
‘tekilleşme’lerden, inşa eyleminin iskânın önüne geçmesi veya binalar aracılığıyla anlam kurma çabalarından topoğrafya da payını düşeni
almaktadır. İstanbul gibi büyük şehirlerde ise, neo-liberal politikalar, küresel ekonomi, popüler kültür gibi etkilerle ilerleyen yapılaşma
sürecinde, topoğrafyanın, -anlamı bir yana- biçimsel boyutuyla dahi ihmal edildiği görülmekte. Bu çalışma genel bir tahlilde, bir yandan
kavramsallaşma ekseninde ilerleyen mimarlık dünyası, öte yandan hızla ilerleyen yapım çalışmaları arasında sıkışan topoğrafyanın, bir
kavram veya bir veri olmasının ötesinde taşıdığı anlamının yeniden sorgulanmasına işaret eder. Topoğrafyanın mimarlıkla olan ilişkisine
dair anlam arayışında yeniden bir sorgulamanın kaçınılmazlığı vurgulanırken, indirgemeci ve yüzeysel bir topoğrafya bilgisinden öte,
mimarlığın özünde yatan kapsayıcı bir kavrayışın önemine işaret edilmiştir. Makale, bu kavrayış sürecinde topoğrafyanın anlamının, topolojik (biçimsel), ekolojik ve coğrafi, ekonomik, sosyo-kültürel, estetik, psikolojik (algı-imaj) ve felsefi bağlamlarda okunması gerektiği önermesine dayanmaktadır
The effect of the business climate on a firm’s inventory performance:a cross-country analysis
No full textIn recent years, the business climate effects on firm performance and profitability started gainingincreasingattentioninbothdevelopedanddevelopingeconomies. However, most of the past studies have been limited to investigating the possible effects of industrial policy and overall business climate on overall firm performance without questioning how the relationship is built. This thesis aims to bring a new, and much needed, perspective on this macro-micro economic relationship. The study aims to answer this important research question by analyzing and understanding to which extent to business climate, a macro level variable, can have a direct effect on inventory performance of a firm, a micro level firm operation. We hypothesize that the aggregate inventory level of a firm would be positively associated with the perceived obstacles in "infrastructure", "bureaucracy", and "finance". At the country level, we hypothesize that the country’s development level would moderate that relationship and increase that positive effect.
Cross-country analysis is performed on the Eastern European and Central Asian region using the data from 2013. Empirical analysis techniques are used utilizing the World Bank’s Enterprise Surveys’ database as well as The Heritage Foundation’s annual reports on 14,000 firms in 24 countries. A Hierarchical Linear Model is developed using IBM SPSSR
statistical software, and seven business climate indicators are used as proxy for constraints realized in "infrastructure", "bureaucracy", and "finance" variables. Days of inventory is used as an indicator of the inventory performance of a company, and as our response variable.
Significantrelationshipsbetweenbusinessclimateandinventoryperformancearesignaled by this study, and the results confirm our hypotheses in general. The results prompt improvements in the business climate to raise competitiveness by directing firms’ operational efficiency. This study can primarily help firms to understand how their operations are consciously or unconsciously related to the business environment of the country. On a macro level, correctly directed operational policies can lead to an increase in firm-level performance, provide a sustainable growth outlook through higher efficiency, and diminish the severe unemployment problems of a country by encouraging both domestic and foreign investments.Declaration of Authorship ii
Abstract iv
Öz vi
Acknowledgments vii
List of Figures x
List of Tables xi
Abbreviations xii
1 Introduction 1
2 Theoretical Background and Hypotheses 4 2.1 Inventory Performance Predictors . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Inventory Management and Firm Performance Relationship . . . . . . . . 8 2.3 Business Climate Effects on Firm Performance, Productivity and/or Growth 10 2.4 Business Climate Effects on Inventory Performance: Hypotheses Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5 Country-level Effects on the Relationship between Business Climate and Inventory Performance: Hypotheses Development . . . . . . . . . . . . . . 17
3 Data Description 19 3.1 The World Bank’s Enterprise Surveys . . . . . . . . . . . . . . . . . . . . 19 3.2 The Heritage Foundation . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3 Eastern Europe and Central Asia . . . . . . . . . . . . . . . . . . . . . . . 22
4 Measurement and Sample 24 4.1 Sample Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.1 Dependent Variable . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.2 Independent (Explanatory) Variables . . . . . . . . . . . . . . . . . 26 4.1.3 Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5 Model and Empirical Results 32 5.1 Hierarchical Linear Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.2 Model Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.2.1 Level 1 (Firm-level) Equation . . . . . . . . . . . . . . . . . . . . . 33 5.2.2 Level 2 (Country-level) Equations . . . . . . . . . . . . . . . . . . . 34 5.2.2.1 First Group (GDP and Direct Effect Only) . . . . . . . . 34 5.2.2.2 Second Group (GDP with Direct Effect + Moderation Effect) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5.2.2.3 Third Group (FFC and Direct Effect Only) . . . . . . . . 36 5.2.2.4 Fourth Group (FFC with Direct Effect + Moderation Effect) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
6 Conclusion 45
A Descriptive Statistics by Country 47
Bibliography 5
Domain name valuation:characteristics & price exposed!
No full textGiven only the domain name, can we predict its price? This is the main question that is examined within the scope of this thesis. Price prediction is one of the very well studied applications of machine learning (ML). An accurate ML approach for price prediction would need a good set of features to represent characteristics that effect the price. Price of a domain name depends not only on its characteristics such as length, language and extension, but also how much a person is willing to pay for it. This introduces a significant uncertainty in domain name valuation and creates a challenging problem to deal with. Additionally, domain names are in a special form of an unseparated text that can only consist of letters, numbers, hyphens and emojis, and with an additional structural limitation on having length of sixty-three characters at most in its puny-coded representation. Exposing all decisive characteristics of a domain name that affect its value consequently proves to be a very challenging task. An extensive domain name sale history dataset is collected as part of this study and numerous unique features are extracted based on domain name.
One of the crucial steps in feature extraction is the identification of words in the domain name. This process includes language identification and word segmentation step that is referred to as Domain Name Language Detection (DNLD) in this thesis work. Identification of domain language and the extraction of words within a domain name is essential in representing the domain name characteristics that have profound effect on its value such as the number of words and the popularity of words used in domain name. DNLD utilizes Fasttext dataset from Facebook [1, 2] and can support up to 265 languages.Declaration of Authorship ii
Abstract iv
Öz v
Acknowledgments vii
List of Figures xi
List of Tables xii
Abbreviations xiv
1 Introduction 1
2 Related Work 6 2.1 Domain Name Language Detection (DNLD) ................. 6 2.2 Domain Name Price Estimation (DNPE) ................... 8
3 Overall Flow 10
4 Data Preparation 14 4.1 Data Collection ................................. 14 4.1.1 DNLD Datasets ............................. 14 4.1.1.1 Fasttext Datasets ...................... 14 4.1.1.2 Noktadomains.com Dataset ................. 15 4.1.2 Sale Dataset............................... 16 4.2 Data Wrangling ................................. 18 4.2.1 Preparing DNLD Look-up Datasets .................. 18 4.3 Feature Extraction ............................... 19 4.3.1 Sale Related Features ......................... 20 4.3.1.1 Venue (i.e. Marketplace) .................. 20 4.3.1.2 Sale Date ........................... 20 4.3.1.2.1 Sale Month ..................... 20 4.3.1.2.2 Sale Season ..................... 21 4.3.1.2.3 Sale Weekday .................... 22 4.3.2 Domain Name Related Features .................... 23 4.3.2.1 Name ............................. 23
4.3.2.2 IDN Status .......................... 24 4.3.2.3 Extension ........................... 24 4.3.2.4 Extension Type ....................... 24 4.3.2.5 Extension Popularity .................... 25 4.3.2.6 Domain Length ........................ 26 4.3.2.7 Contains Letter ....................... 27 4.3.2.8 Letter Count ......................... 27 4.3.2.9 Contains Number ...................... 28 4.3.2.10 Number Count ........................ 28 4.3.2.11 Contains Hyphen ....................... 29 4.3.2.12 Hyphen Count ........................ 29 4.3.2.13 Contains Emoji........................ 30 4.3.2.14 Emoji Count ......................... 31 4.3.3 Extension Stats Related Features ................... 31 4.3.3.1 Available Extension Count ................. 31 4.3.3.2 Forsale Extension Count................... 32 4.3.3.3 Registered Extension Count ................. 32 4.3.4 DNLD Related Features ........................ 33 4.3.4.1 Domain Language ...................... 33 4.3.4.2 Keywords ........................... 34 4.3.4.3 Number of Keywords .................... 35 4.3.4.4 Meaningful Char Count ................... 36 4.3.4.5 Unmeaningful Char Count ................. 37 4.3.4.6 Meaningfulness Ratio .................... 38 4.3.4.7 Has Unassigned Char .................... 39 4.3.4.8 Keyword Length ....................... 39 4.3.4.8.1 Min. Keyword Length ............... 39 4.3.4.8.2 Avg. Keyword Length ............... 40 4.3.4.8.3 Max. Keyword Length ............... 41 4.3.4.9 Meaningful Language Count ................ 42 4.3.4.10 Partially Meaningful Language Count ........... 43 4.3.4.11 Keyword Popularity ..................... 43 4.3.4.11.1 Min. Keyword Popularity ............. 44 4.3.4.11.2 Avg. Keyword Popularity ............. 44 4.3.4.11.3 Max. Keyword Popularity ............. 44 4.4 Column Transformation ............................ 45 4.4.1 Numeric Feature Transformation ................... 45 4.4.2 Categorical Feature Transformation.................. 45 4.4.3 Text Feature Transformation ..................... 46 4.4.4 Ngram Representation ......................... 46
5 Methodology & Modeling 47 5.1 Domain Name Language Detection (DNLD) ................. 47 5.1.1 DNLD as a Micro-service ....................... 51 5.2 Domain Name Price Estimation (DNPE) ................... 52
6 Results & Discussion 54
6.1 DNLD ...................................... 54 6.1.1 Keyword Extraction .......................... 54 6.1.2 Language Detection .......................... 56 6.2 DNPE ...................................... 60 6.2.1 Eliminating Outliers .......................... 61 6.2.1.1 Standard Deviation Method (STD)............. 61 6.2.1.2 Center 99% .......................... 62 6.2.1.3 Interquartile Range Method (IQR) ............. 63
7 Conclusion 66
8 Future Work 68
Bibliography 7
Ortaöğretim kurumlarında uygulanan siber güvenlik farkındalık eğitiminin öğrenciler üzerindeki etkisi
No full textComputers are in every sphere of life and all data is shared and stored through computers. Everyone, from government institutions to enterprises and individuals, must be aware of cybersecurity. Otherwise, economic problems may arise, information theft and resource consumption may increase, and crimes such as fraud may be multiplied. In addition to all these, the security of the countries may be compromised. Therefore, the education of children as tomorrow's decision makers on cybersecurity, which has become a national issue, will be e ective in reducing individual risks. The reduction of individual risks will also be directly proportional to the reduction of national cybercrime risks. In this study, cybersecurity strategies of the EU, USA and Turkey, especially in the eld of education, have been examined and compared with each other. In the eld research aimed at reducing individual cyber risks, 211 Middle School 5th grade students were given cybersecurity awareness training. Sensitivity scales for personal safety and cyberbullying were used. The difierence was measured by questionnaires conducted before and after the training. The cybersecurity awareness training given has been shown to have a positive effect on the awareness of cyber-bullying and providing personal cybersecurity for secondary school students.Yazarlk Beyan ii
Öz iii
Äbstract iv
Te³ekkür v
ekil Listesi viii
Tablo Listesi ix
Ksaltmalar x
1 Giri³ 1
2 Bilgi Güvenli§i 7 2.1 Bilgi Güvenli§i Tanm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Bilgi Güvenli§inin Geli³imi . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3 Türkiye'de Ya³anan Bilgi Güvenli§i Olaylar . . . . . . . . . . . . . . . . . 12 2.3.1 Atatürk Havaliman Zararl Yazlm Ocak 2009 . . . . . . . . . . . 13 2.3.2 Nic.Tr DDoS Saldrs Aralk 2015 . . . . . . . . . . . . . . . . . . 14 2.3.3 Sa§lk Bakanl§ Hastanelerine Yönelik Siber Saldrlar Mays 2016 16 2.4 Dünya'da Ya³anan Siber Güvenlik Olaylar . . . . . . . . . . . . . . . . . 17 2.4.1 Estonya Saldrs Nisan 2007 . . . . . . . . . . . . . . . . . . . . . . 18 2.4.2 Sony Saldrs Aralk 2014 . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.3 ABD DDoS Saldrs Ekim 2016 . . . . . . . . . . . . . . . . . . . . 20
3 Amerika'nn Siber Güvenlik Stratejilerinin ncelenmesi 21 3.1 Genel bak³ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Kamu-Özel Sektör ³birlikleri ve Sorumlu Kurumlar . . . . . . . . . . . . . 22 3.3 E§itim ve Farkndalk Çal³malar . . . . . . . . . . . . . . . . . . . . . . . 25
4 Avrupa Birli§i'nin Siber Güvenlik Stratejisinin ncelenmesi 27 4.1 Genel bak³ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2 E§itim ve Farkndalk Çal³malar . . . . . . . . . . . . . . . . . . . . . . . 28 4.2.1 European Cyber Security Month . . . . . . . . . . . . . . . . . . . 29 4.2.2 Kritik Altyap Uyar Bilgi A§ (CIWIN) . . . . . . . . . . . . . . . 30 4.3 Kamu-Özel Sektör ³birlikleri ve Sorumlu Kurumlar . . . . . . . . . . . . . 30
5 Türkiye'nin 2016 2019 Siber Güvenlik Stratejisinin ncelenmesi 32 5.1 Genel Bak³ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.2 E§itim ve Farkndalk Çal³malar . . . . . . . . . . . . . . . . . . . . . . . 37 5.3 Kar³la³trma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.4 Farkndal§ Arttracak Kitlelerin Tespit Edilmesi . . . . . . . . . . . . . . 39
6 Alan Ara³trmas 42 6.1 Yöntem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 6.2 Ara³trma Modeli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.3 Veri Toplama Araçlar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.4 Örneklem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6.5 Verilerin De§erlendirilmesi . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
7 Bulgular 46
8 Sonuç ve Öneriler 58 8.1 Sonuç . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 8.2 Öneriler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
A Ekler 62
Kaynakça 6