The Internet and Drug Markets
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1 of 6 C O R P O R a T E Amazon/Acquisitions the Chart
CORPORATE Amazon/Acquisitions The chart below delineates Amazon’s biggest acquisitions, according to Thomson Reuters Deal Intelligence, as reported by The Wall Street Journal: Company Deal Value (Billions) Whole Foods MarKet $13.70 Zappos.com $1.20 Twitch Interactive $0.97 Kiva Systems $0.78 Souq.com $0.70 Quidsi $0.55 Elemental Technologies $0.30 Atlas Air Worldwide $0.28 Alexa Internet $0.26 Exchange.com $0.25 SoftBanK/Charter In early August, Charter Communications passed on the idea of acquiring Sprint, which is majority owned by SoftBanK, a Japanese telecommunications company. Last week, rumors began to circulate that SoftBank is exploring a complex taKeover of Charter. FAST FOOD Little Caesars/Portal Pizza chain Little Caesars introduce The Pizza Portal, a machine that lets Machine customers who order pies to skip the line, grab their pizza and go in more than a dozen locations in the Tucson (AZ) area. How it worKs: download an app to order and pay for food, receive a three digit or QR code and once in the store enter or scan the code to open a self-service hot box at the shop. GLOBAL Nuclear Warheads Estimated nuclear warhead inventories, as reported by The Wall Street Journal: Country Warheads Country Warheads Russia 7,000 Pakistan 140 U.S. 6,800 India 130 France 300 Israel 80 China 270 North Korea 10* U.K. 215 Pakistan 140 *Projection 1 of 6 MOVIES TicKet Sales According to Nielsen, North American box office ticKet sales are down 2.9% even with ticKet prices higher than the prior year – maKing up for some of the diminished theater attendance. -
The Current Economics of Cyber Attacks
The Current Economics of Cyber Attacks Ron Winward Security Evangelist October 17, 2016 What Are We Talking About Historical Context Does Hacking Pay? Cyber Aack Marketplace Economics of Defenses: Reality Check 2 Once Upon a Time… 3 Story of the Automobile Ideal economic condions help fuel and grow this industry Be&er Roads Assembly Line Ideal Economic Condi9ons Growth 4 Cyber Attacks Reaching a Tipping Point More Resources More Targets More Mature Availability of low More high value, A level of maturity cost resources increasingly vulnerable that drives efficiency targets leads to more and ensures valuable informaon anonymity The economics of hacking have turned a corner! 5 Modern Economics of Cyber Attacks and Hacking 6 Do You Romanticize Hackers? 7 Reality of Today’s Hackers May look more like this . than like this 8 Today’s Adversary: Not always the Lone Wolf Structured organizaon with roles, focus Premeditated plan for targe9ng, exfiltraon, mone9zaon of data/assets Mul9-layered trading networks for distribu9on, Source: HPE: the business of hacking obfuscaon Why? Because increasingly, CRIME PAYS! 9 Or Does It? The average aacker earns approximately ¼ of the salary of an average IT employee The cost and 9me to plan aacks has decreased Be&er access to be&er tools makes aacks easier Remember: Nobody is trying to be “average” 10 Sophisticated Understanding of Value Mone9zable criminal enterprise Credit Cards Medical Records Intellectual Property Creden9als Vulnerabili9es Exploits 11 The Economics of Web Attacks Hacker steals US healthcare -
Virtual Currencies and Terrorist Financing : Assessing the Risks And
DIRECTORATE GENERAL FOR INTERNAL POLICIES POLICY DEPARTMENT FOR CITIZENS' RIGHTS AND CONSTITUTIONAL AFFAIRS COUNTER-TERRORISM Virtual currencies and terrorist financing: assessing the risks and evaluating responses STUDY Abstract This study, commissioned by the European Parliament’s Policy Department for Citizens’ Rights and Constitutional Affairs at the request of the TERR Committee, explores the terrorist financing (TF) risks of virtual currencies (VCs), including cryptocurrencies such as Bitcoin. It describes the features of VCs that present TF risks, and reviews the open source literature on terrorist use of virtual currencies to understand the current state and likely future manifestation of the risk. It then reviews the regulatory and law enforcement response in the EU and beyond, assessing the effectiveness of measures taken to date. Finally, it provides recommendations for EU policymakers and other relevant stakeholders for ensuring the TF risks of VCs are adequately mitigated. PE 604.970 EN ABOUT THE PUBLICATION This research paper was requested by the European Parliament's Special Committee on Terrorism and was commissioned, overseen and published by the Policy Department for Citizens’ Rights and Constitutional Affairs. Policy Departments provide independent expertise, both in-house and externally, to support European Parliament committees and other parliamentary bodies in shaping legislation and exercising democratic scrutiny over EU external and internal policies. To contact the Policy Department for Citizens’ Rights and Constitutional Affairs or to subscribe to its newsletter please write to: [email protected] RESPONSIBLE RESEARCH ADMINISTRATOR Kristiina MILT Policy Department for Citizens' Rights and Constitutional Affairs European Parliament B-1047 Brussels E-mail: [email protected] AUTHORS Tom KEATINGE, Director of the Centre for Financial Crime and Security Studies, Royal United Services Institute (coordinator) David CARLISLE, Centre for Financial Crime and Security Studies, Royal United Services Institute, etc. -
Fully Automatic Link Spam Detection∗ Work in Progress
SpamRank – Fully Automatic Link Spam Detection∗ Work in progress András A. Benczúr1,2 Károly Csalogány1,2 Tamás Sarlós1,2 Máté Uher1 1 Computer and Automation Research Institute, Hungarian Academy of Sciences (MTA SZTAKI) 11 Lagymanyosi u., H–1111 Budapest, Hungary 2 Eötvös University, Budapest {benczur, cskaresz, stamas, umate}@ilab.sztaki.hu www.ilab.sztaki.hu/websearch Abstract Spammers intend to increase the PageRank of certain spam pages by creating a large number of links pointing to them. We propose a novel method based on the concept of personalized PageRank that detects pages with an undeserved high PageRank value without the need of any kind of white or blacklists or other means of human intervention. We assume that spammed pages have a biased distribution of pages that contribute to the undeserved high PageRank value. We define SpamRank by penalizing pages that originate a suspicious PageRank share and personalizing PageRank on the penalties. Our method is tested on a 31 M page crawl of the .de domain with a manually classified 1000-page stratified random sample with bias towards large PageRank values. 1 Introduction Identifying and preventing spam was cited as one of the top challenges in web search engines in a 2002 paper [24]. Amit Singhal, principal scientist of Google Inc. estimated that the search engine spam industry had a revenue potential of $4.5 billion in year 2004 if they had been able to completely fool all search engines on all commercially viable queries [36]. Due to the large and ever increasing financial gains resulting from high search engine ratings, it is no wonder that a significant amount of human and machine resources are devoted to artificially inflating the rankings of certain web pages. -
Clique-Attacks Detection in Web Search Engine for Spamdexing Using K-Clique Percolation Technique
International Journal of Machine Learning and Computing, Vol. 2, No. 5, October 2012 Clique-Attacks Detection in Web Search Engine for Spamdexing using K-Clique Percolation Technique S. K. Jayanthi and S. Sasikala, Member, IACSIT Clique cluster groups the set of nodes that are completely Abstract—Search engines make the information retrieval connected to each other. Specifically if connections are added task easier for the users. Highly ranking position in the search between objects in the order of their distance from one engine query results brings great benefits for websites. Some another a cluster if formed when the objects forms a clique. If website owners interpret the link architecture to improve ranks. a web site is considered as a clique, then incoming and To handle the search engine spam problems, especially link farm spam, clique identification in the network structure would outgoing links analysis reveals the cliques existence in web. help a lot. This paper proposes a novel strategy to detect the It means strong interconnection between few websites with spam based on K-Clique Percolation method. Data collected mutual link interchange. It improves all websites rank, which from website and classified with NaiveBayes Classification participates in the clique cluster. In Fig. 2 one particular case algorithm. The suspicious spam sites are analyzed for of link spam, link farm spam is portrayed. That figure points clique-attacks. Observations and findings were given regarding one particular node (website) is pointed by so many nodes the spam. Performance of the system seems to be good in terms of accuracy. (websites), this structure gives higher rank for that website as per the PageRank algorithm. -
Fraud and the Darknets
OFFICE OF THE INSPECTOR GENERAL U.S. Department of Education Technology Crimes Division Fraud And The Darknets Thomas Harper Assistant Special Agent in Charge Technology Crimes Division OFFICE OF THE INSPECTOR GENERAL U.S. Department of Education Technology Crimes Division What is an OIG? • Established by Congress • Independent agency that reports to Congress • Agency head appointed by the President and confirmed by Congress • Mission: protect the taxpayer’s interests by ensuring the integrity and efficiency of the associated agency OFFICE OF THE INSPECTOR GENERAL U.S. Department of Education Technology Crimes Division Technology Crimes Division • Investigate criminal cyber threats against the Department’s IT infrastructure, or • Criminal activity in cyber space that threatens the Department’s administration of Federal education assistance funds • Investigative jurisdiction encompasses any IT system used in the administration of Federal money originating from the Department of Education. OFFICE OF THE INSPECTOR GENERAL U.S. Department of Education Technology Crimes Division Work Examples • Grade hacking • Computer Intrusions • Criminal Forums online selling malware • ID/Credential theft to hijack Student Aid applications • Misuse of Department systems to obtain personal information • Falsifying student aid applications by U.S. government employees • Child Exploitation material trafficking OFFICE OF THE INSPECTOR GENERAL U.S. Department of Education Technology Crimes Division Fraud and the Darknets Special Thanks to Financial Crimes Enforcement Network (FINCEN) OFFICE OF THE INSPECTOR GENERAL U.S. Department of Education Technology Crimes Division Fraud and the Darknets OFFICE OF THE INSPECTOR GENERAL U.S. Department of Education Technology Crimes Division OFFICE OF THE INSPECTOR GENERAL U.S. Department of Education Technology Crimes Division OFFICE OF THE INSPECTOR GENERAL U.S. -
Support for Transitions to Address Barriers to Student Learning
A Center Training Tutorial . SUPPORT FOR TRANSITIONS TO ADDRESS BARRIERS TO STUDENT LEARNING This document is a hardcopy version of a resource that can be downloaded at no cost from the Center’s website http://smhp.psych.ucla.edu. This Center is co-directed by Howard Adelman and Linda Taylor and operates under the auspice of the School Mental Health Project, Dept. of Psychology, UCLA. Center for Mental Health in Schools, Box 951563, Los Angeles, CA 90095-1563 (310) 825-3634 Fax: (310) 206-8716; E-mail: [email protected] Website: http://smhp.psych.ucla.edu Support comes in part from the Office of Adolescent Health, Maternal and Child Health Bureau (Title V, Social Security Act), Health Resources and Services Administration (Project #U45 MC 00175). Continuing Education Modules & Training Tutorials: Self-directed opportunities to learn In addition to offering Quick Training Aids, the Center’s Continuing Education Modules and Training Tutorials are designed as self-directed opportunities for more in-depth learning about specific topics. These resources provide easy access to a wealth of planfully organized content and tools that can be used as a self-tutorial or as a guide in training others. As with most of our resources, these can be readily downloaded from our website – http://smhp.psych.ucla.edu – see Center Materials and scroll down to VI. In the coming years, the Center will continue to develop a variety of continuing education modules and training tutorials related to the various topics covered by our Clearinghouse. In all its work, the Center tries to identify resources that represent "best practice" standards. -
An Evolving Threat the Deep Web
8 An Evolving Threat The Deep Web Learning Objectives distribute 1. Explain the differences between the deep web and darknets.or 2. Understand how the darknets are accessed. 3. Discuss the hidden wiki and how it is useful to criminals. 4. Understand the anonymity offered by the deep web. 5. Discuss the legal issues associated withpost, use of the deep web and the darknets. The action aimed to stop the sale, distribution and promotion of illegal and harmful items, including weapons and drugs, which were being sold on online ‘dark’ marketplaces. Operation Onymous, coordinated by Europol’s Europeancopy, Cybercrime Centre (EC3), the FBI, the U.S. Immigration and Customs Enforcement (ICE), Homeland Security Investigations (HSI) and Eurojust, resulted in 17 arrests of vendors andnot administrators running these online marketplaces and more than 410 hidden services being taken down. In addition, bitcoins worth approximately USD 1 million, EUR 180,000 Do in cash, drugs, gold and silver were seized. —Europol, 20141 143 Copyright ©2018 by SAGE Publications, Inc. This work may not be reproduced or distributed in any form or by any means without express written permission of the publisher. 144 Cyberspace, Cybersecurity, and Cybercrime THINK ABOUT IT 8.1 Surface Web and Deep Web Google, Facebook, and any website you can What Would You Do? find via traditional search engines (Internet Explorer, Chrome, Firefox, etc.) are all located 1. The deep web offers users an anonym- on the surface web. It is likely that when you ity that the surface web cannot provide. use the Internet for research and/or social What would you do if you knew that purposes you are using the surface web. -
Into the Reverie: Exploration of the Dream Market
Into the Reverie: Exploration of the Dream Market Theo Carr1, Jun Zhuang2, Dwight Sablan3, Emma LaRue4, Yubao Wu5, Mohammad Al Hasan2, and George Mohler2 1Department of Mathematics, Northeastern University, Boston, MA 2Department of Computer & Information Science, Indiana University - Purdue University, Indianapolis, IN 3Department of Mathematics and Computer Science, University of Guam, Guam 4Department of Mathematics and Statistics, University of Arkansas at Little Rock, AK 5Department of Computer Science, Georgia State University, Atlanta, GA [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected], [email protected] Abstract—Since the emergence of the Silk Road market in Onymous" in 2014, a worldwide action taken by law enforce- the early 2010s, dark web ‘cryptomarkets’ have proliferated and ment and judicial agencies aimed to put a kibosh on these offered people an online platform to buy and sell illicit drugs, illicit behaviors [5]. Law enforcement interventions such as relying on cryptocurrencies such as Bitcoin for anonymous trans- actions. However, recent studies have highlighted the potential for Onymous, along with exit scams and hacks, have successfully de-anonymization of bitcoin transactions, bringing into question shut down numerous cryptomarkets, including AlphaBay, Silk the level of anonymity afforded by cryptomarkets. We examine a Road, Dream, and more recently, Wall Street [6]. Despite these set of over 100,000 product reviews from several cryptomarkets interruptions, new markets have continued to proliferate. The collected in 2018 and 2019 and conduct a comprehensive analysis authors of [7] note that there appears to be a consistent daily of the markets, including an examination of the distribution of drug sales and revenue among vendors, and a comparison demand of about $500,000 for illicit products on the dark web, of incidences of opioid sales to overdose deaths in a US city. -
Honeypots: Not for Winnie the Pooh But
2018] 259 HONEYPOTS: NOT FOR WINNIE THE POOH BUT FOR WINNIE THE PEDO — LAW ENFORCEMENT’S LAWFUL USE OF TECHNOLOGY TO CATCH PERPETRATORS AND HELP VICTIMS OF CHILD EXPLOITATION ON THE DARK WEB Whitney J. Gregory* Cruelty, like every other vice, requires no motive outside itself—it only requires opportunity.1 INTRODUCTION Lawyers, doctors, teachers, politicians, and Hollywood stars—what do they all have in common? Smarts? Success? Wealth? Respect in their com- munities? Demonstrating the terrible divergence between appearance and re- ality, some members of these professions are also frequent customers and producers of child pornography. Contrary to what some may assume, child pornographers are not just antisocial, out-of-work, reclusive basement dwellers. They may be people you would least expect.2 Take, for instance, the teen heartthrob Mark Salling, who starred on Fox’s hit show Glee as handsome bad-boy Puck from 2009 to 2015. Twenty-fifteen was also the year Salling was arrested and charged * Antonin Scalia Law School at George Mason University, J.D. Candidate, May 2019; Articles Editor, George Mason Law Review, 2018–19. This Comment is dedicated to the memory of my grandfa- ther Guido A. Ianiero. 1 George Eliot, Janet’s Repentance, in SCENES OF CLERICAL LIFE 102, 146 (Harper & Bros. 1858). 2 Judges have described child pornography defendants as seemingly ordinary, even upstanding, men (and a few women). “The defendants’ professional careers [are] often highlighted, including Air Force Captain, physician, trust specialist, and teacher.” Melissa Hamilton, The Efficacy of Severe Child Pornography Sentencing: Empirical Validity or Political Rhetoric?, 22 STAN. -
TRUST, but VERIFY: WHY the BLOCKCHAIN NEEDS the LAW Kevin Werbach†
TRUST, BUT VERIFY: WHY THE BLOCKCHAIN NEEDS THE LAW Kevin Werbach† ABSTRACT The blockchain could be the most consequential development in information technology since the Internet. Created to support the Bitcoin digital currency, the blockchain is actually something deeper: a novel solution to the age-old human problem of trust. Its potential is extraordinary. Yet, this approach may not promote trust at all without effective governance. Wholly divorced from legal enforcement, blockchain-based systems may be counterproductive or even dangerous. And they are less insulated from the law’s reach than it seems. The central question is not how to regulate blockchains but how blockchains regulate. They may supplement, complement, or substitute for legal enforcement. Excessive or premature application of rigid legal obligations will stymie innovation and forego opportunities to leverage technology to achieve public policy objectives. Blockchain developers and legal institutions can work together. Each must recognize the unique affordances of the other system. DOI: https://doi.org/10.15779/Z38H41JM9N © 2018 Kevin Werbach. † Associate Professor of Legal Studies and Business Ethics, The Wharton School, University of Pennsylvania. Email: [email protected]. Thanks to Dan Hunter for collaborating to develop the ideas that gave rise to this Article, and to Sarah Light, Patrick Murck, and participants in the 2017 Lastowka Cyberlaw Colloquium and 2016 TPRC Conference for comments on earlier drafts. 488 BERKELEY TECHNOLOGY LAW JOURNAL [Vol. 33:487 -
USA -V- Julian Assange Judgment
JUDICIARY OF ENGLAND AND WALES District Judge (Magistrates’ Court) Vanessa Baraitser In the Westminster Magistrates’ Court Between: THE GOVERNMENT OF THE UNITED STATES OF AMERICA Requesting State -v- JULIAN PAUL ASSANGE Requested Person INDEX Page A. Introduction 2 a. The Request 2 b. Procedural History (US) 3 c. Procedural History (UK) 4 B. The Conduct 5 a. Second Superseding Indictment 5 b. Alleged Conduct 9 c. The Evidence 15 C. Issues Raised 15 D. The US-UK Treaty 16 E. Initial Stages of the Extradition Hearing 25 a. Section 78(2) 25 b. Section 78(4) 26 I. Section 78(4)(a) 26 II. Section 78(4)(b) 26 i. Section 137(3)(a): The Conduct 27 ii. Section 137(3)(b): Dual Criminality 27 1 The first strand (count 2) 33 The second strand (counts 3-14,1,18) and Article 10 34 The third strand (counts 15-17, 1) and Article 10 43 The right to truth/ Necessity 50 iii. Section 137(3)(c): maximum sentence requirement 53 F. Bars to Extradition 53 a. Section 81 (Extraneous Considerations) 53 I. Section 81(a) 55 II. Section 81(b) 69 b. Section 82 (Passage of Time) 71 G. Human Rights 76 a. Article 6 84 b. Article 7 82 c. Article 10 88 H. Health – Section 91 92 a. Prison Conditions 93 I. Pre-Trial 93 II. Post-Trial 98 b. Psychiatric Evidence 101 I. The defence medical evidence 101 II. The US medical evidence 105 III. Findings on the medical evidence 108 c. The Turner Criteria 111 I.