Weapons in the Dark: Dark Net Demand and Supply Response to Mass-Casualty Events
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COPENHAGEN BUSINESS SCHOOL MSc Advanced Economics and Finance Master’s Thesis Weapons in the Dark: Dark Net Demand and Supply Response to Mass-casualty Events. Authors: Elena Ivanchenkova, Lavi Apfelbaum Supervisor: Chandler Lutz Date of submission: May 15th, 2017. Number of Pages: 100 Number of Characters: 164,460 May 2017 Illustration source: Scotti, 2013. \Place your clothes and weapons where you can find them in the dark." Robert A. Heinlein COPENHAGEN BUSINESS SCHOOL Abstract by Elena Ivanchenkova & Lavi Apfelbaum In this paper, we examine the effect of mass-casualty events on the supply and demand for arms on the Dark Net markets. We explain the environment based on the existing research literature on Bitcoin, encrypted browsers, smuggling and other mechanisms that keep it functioning. We overcome data quality issues related to missing observation using imputation tools. In this research, we use classical econometric methods to obtain evidence on the weapons trade response to the non-strict exogenous shocks. Our results provide some evidence on the short-term and midterm arms sales reaction to the terror attacks and mass shootings. We also find a potential of recognising trends and patterns in weapon trade through the activity analysis in the Dark Net markets Acknowledgements We hereby thank our thesis supervisor Chandler Lutz for the continuous support his guidance and engagement in the process of this research. We furthermore thank Gwern Branwen for his advice and being open in sharing the knowledge not only about the dataset but on the research field in general. Finally, we thank our family and friends who supported us throughout the process and provided some valuable ideas which we implemented in the research. iii Contents Abstract ii Acknowledgements iii List of Figures vii List of Tables ix Abbreviationsx 1 Introduction1 1.1 Research question and contribution.......................2 1.2 Research methodology and scientific approach.................3 1.3 Outline......................................3 2 Dark net markets set up4 2.1 History and development............................4 2.1.1 Black markets history and development................4 2.1.2 DNMs history and development.....................6 2.1.3 Terminology...............................7 2.1.4 Challenges to law enforcement.....................8 2.2 Dark Net tools..................................9 2.2.1 The Onion Router............................ 10 2.2.2 Bitcoin.................................. 15 2.2.3 Encryption................................ 18 2.2.4 Escrow................................... 21 2.2.5 Delivery mechanisms to end users................... 22 3 Literature review 24 3.1 Market mechanisms on the Dark Net...................... 24 3.2 Properties of Bitcoin............................... 27 iv Contents v 3.3 Terrorism..................................... 30 3.4 Weapon laws and illicit trade.......................... 32 4 Theory 34 4.1 Hypothesis.................................... 34 4.1.1 Main hypothesis............................. 34 4.1.2 Secondary hypothesis.......................... 36 4.2 Model setup.................................... 36 4.2.1 Variables for hypothesis testing..................... 36 4.2.2 Weapon price return........................... 37 4.2.3 Dynamic causality effect......................... 38 4.2.3.1 Difference in number of market listing per day....... 39 4.2.3.2 Difference in aggregated USD value of market listing per day 39 4.2.3.3 Average Bitcoin return per day adjusted for exchange rates effects.............................. 40 4.2.3.4 Number of new market listings per day........... 41 4.3 Assumptions................................... 42 5 Data description 44 5.1 Data set composition............................... 44 5.1.1 General considerations for the data................... 44 5.2 Dependent variable Y. Market listings...................... 45 5.2.1 Data source................................ 45 5.2.2 The crawler................................ 46 5.2.3 Data preparation for the model..................... 49 5.2.4 Market listings data quality considerations.............. 51 5.3 Explanatory X variables............................. 52 5.3.1 Exchange rates and precious metals data............... 52 5.3.2 Number of casualties from terror attacks and mass shootings.... 53 6 Methodology 55 6.1 Imputation and handling missing data..................... 55 6.2 Models implementation............................. 57 6.2.1 Weapon price return........................... 57 6.2.2 Difference in number of market listing per day............ 58 6.2.3 Difference in aggregated USD value of market listing per day.... 59 6.2.3.1 Average Bitcoin return per day adjusted for exchange rates effects.............................. 61 6.2.3.2 Number of new market listings per day........... 62 7 Analysis 65 7.1 Weapon price return............................... 65 7.1.1 Currencies significance specification.................. 65 Contents vi 7.2 Number of market listings per day....................... 69 7.3 Aggregated USD value of market listings per day............... 71 7.4 Average bitcoin return per day adjusted for exchange rates effects..... 73 7.5 The number of new market listings....................... 77 8 Discussion 83 8.1 Robustness of the assumptions......................... 83 8.2 Limitations.................................... 85 8.3 Results discussion and possible implications.................. 86 8.3.1 Weapon price return........................... 86 8.3.2 Number of new market listings..................... 87 8.4 Future possible research............................. 87 8.4.1 Weapon price return........................... 88 8.4.2 Dynamic causality effect......................... 88 9 Conclusion 89 A Appendix 91 A.1 Dark Net markets set up............................. 91 A.2 Code for filtering the data by product names in python........... 94 A.3 Code for filtering the data by specific vendor names in python........ 98 A.4 Table of casualties................................ 101 A.5 methodology................................... 105 A.6 Analysis...................................... 106 Bibliography 111 List of Figures 2.1 How Tor works part 1. Source: TorAbout................... 12 2.2 How Tor works part 2. Source: TorAbout................... 13 2.3 How Tor works part 3. Source: TorAbout................... 14 2.4 Bank system. Source: He et al.(2016)..................... 16 2.5 Blockchain distributed ledger. Source: He et al.(2016)............ 16 2.6 Blockhain in by the numbers. Source: Blockchain............... 17 2.7 An example of the PGP public key....................... 20 3.1 Volatility of Bitcoin compared to major currencies and gold. Source: Yer- mack(2013).................................... 28 3.2 Bitcoin price difference across exchanges. Source: Kroeger and Sarkar(2016) 29 5.1 DNM listing example. Source: Alphabay.................... 47 5.2 DNM custom listing example. Source: Alphabay............... 50 6.1 Graphical explanation of the imputation process and its analysis...... 56 6.2 Distribution of imputed number of market listings dataset and original dataset 59 6.3 Distribution of imputed aggregated USD value of market listings dataset and original dataset............................... 60 6.4 Distribution of imputed average Bitcoin return per day adjusted for ex- change rates effects................................ 62 6.5 Distribution of imputed number of new market listings dataset and original dataset....................................... 63 6.6 Rounded down distribution of imputed number of new market listings dataset and original dataset............................... 64 7.1 Cumulative sum of the residuals from the weapon price index model.... 75 7.2 ACF for cumulative sum of residuals..................... 77 7.3 PACF of new market listings.......................... 80 A.1 Tor hidden service 1............................... 91 A.2 Tor hidden service 2............................... 92 A.3 Tor hidden service 3............................... 92 A.4 Tor hidden service 4............................... 93 A.5 Tor hidden service 5............................... 93 vii List of Figures viii A.6 Tor hidden service 6............................... 94 A.7 PCA of residuals................................. 105 A.8 Range of possible imputed values for an observed data point......... 106 A.9 ACF of new deals................................ 110 List of Tables 5.1 DNMs that are present in the dataset..................... 46 7.1 Output of the weapon price return model................... 66 7.2 Output of the weapon price return model (continued)............ 67 7.3 The difference in number of listings per day.................. 70 7.4 Difference in standardised value of listings explained by "number of casual- ties"........................................ 72 7.5 Residuals from the first model explained by "number of casualties".... 74 7.6 Augmented Dickey-Fuller test for unit root with a drift........... 76 7.7 Number of new listings explained by the "number of casualties" (under Poisson model).................................. 78 7.8 Number of new listings explained by the "number of casualties"(80 lags).. 79 8.1 Positive and negative coefficients of significant currencies.......... 86 A.1 DNMs that are present in the data set..................... 105 ix Abbreviations DN Dark Net DNM Dark Net Market PGP Pretty Good Privacy ECB European Central Bank GTD Global Terrorism Databse x Chapter 1 Introduction We live in a high-tech era where the internet