Information Retrieval and Web Search Engines
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The Internet and Drug Markets
INSIGHTS EN ISSN THE INTERNET AND DRUG MARKETS 2314-9264 The internet and drug markets 21 The internet and drug markets EMCDDA project group Jane Mounteney, Alessandra Bo and Alberto Oteo 21 Legal notice This publication of the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) is protected by copyright. The EMCDDA accepts no responsibility or liability for any consequences arising from the use of the data contained in this document. The contents of this publication do not necessarily reflect the official opinions of the EMCDDA’s partners, any EU Member State or any agency or institution of the European Union. Europe Direct is a service to help you find answers to your questions about the European Union Freephone number (*): 00 800 6 7 8 9 10 11 (*) The information given is free, as are most calls (though some operators, phone boxes or hotels may charge you). More information on the European Union is available on the internet (http://europa.eu). Luxembourg: Publications Office of the European Union, 2016 ISBN: 978-92-9168-841-8 doi:10.2810/324608 © European Monitoring Centre for Drugs and Drug Addiction, 2016 Reproduction is authorised provided the source is acknowledged. This publication should be referenced as: European Monitoring Centre for Drugs and Drug Addiction (2016), The internet and drug markets, EMCDDA Insights 21, Publications Office of the European Union, Luxembourg. References to chapters in this publication should include, where relevant, references to the authors of each chapter, together with a reference to the wider publication. For example: Mounteney, J., Oteo, A. and Griffiths, P. -
Adsense-Blackhat-Edition.Pdf
AdSense.BlackHatEditionV.coinmc e Tan Vince Tan AdSense.BlackHatEdition.com i Copyright Notice All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical. Any unauthorized duplication, reproduction, or distribution is strictly prohibited and prosecutable by the full-extent of the law. Legal Notice While attempts have been made to verify the information contained within this publication, the author, publisher, and anyone associated with its creation, hereby assume absolutely no responsibility as it pertains to its contents and subject matter; nor with regards to it’s usage by the public or in any matters concerning potentially erroneous and/or contradictory information put forth by it. Furthermore, the reader agrees to assume all accountability for the usage of any information obtained from it; and heretofore, completely absolves Vince Tan, the publishers and any associates involved, of any liability for it whatsoever. Additional Notice: This book was obtained at http://AdSense.BlackHatEdition.com . For a limited time, when you register for free, you will be able to earn your way to get the latest updates of this ebook, placement on an invitation-only list to participate in exclusive pre-sales, web seminars, bonuses & giveaways, and be awarded a “special backdoor discount” that you will never find anywhere else! So if you haven’t yet, make sure to visit http://AdSense.BlackHatEdition.com and register now, before it’s too late! i Table of Contents Introduction -
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. -
Copyrighted Material
00929ftoc.qxd:00929ftoc 3/13/07 2:02 PM Page ix Contents Acknowledgments vii Introduction xvii Chapter 1: You: Programmer and Search Engine Marketer 1 Who Are You? 2 What Do You Need to Learn? 3 SEO and the Site Architecture 4 SEO Cannot Be an Afterthought 5 Communicating Architectural Decisions 5 Architectural Minutiae Can Make or Break You 5 Preparing Your Playground 6 Installing XAMPP 7 Preparing the Working Folder 8 Preparing the Database 11 Summary 12 Chapter 2: A Primer in Basic SEO 13 Introduction to SEO 13 Link Equity 14 Google PageRank 15 A Word on Usability and Accessibility 16 Search Engine Ranking Factors 17 On-Page Factors 17 Visible On-Page Factors 18 Invisible On-Page Factors 20 Time-Based Factors 21 External FactorsCOPYRIGHTED MATERIAL 22 Potential Search Engine Penalties 26 The Google “Sandbox Effect” 26 The Expired Domain Penalty 26 Duplicate Content Penalty 27 The Google Supplemental Index 27 Resources and Tools 28 Web Analytics 28 00929ftoc.qxd:00929ftoc 3/13/07 2:02 PM Page x Contents Market Research 29 Researching Keywords 32 Browser Plugins 33 Community Forums 33 Search Engine Blogs and Resources 34 Summary 35 Chapter 3: Provocative SE-Friendly URLs 37 Why Do URLs Matter? 38 Static URLs and Dynamic URLs 38 Static URLs 39 Dynamic URLs 39 URLs and CTR 40 URLs and Duplicate Content 41 URLs of the Real World 42 Example #1: Dynamic URLs 42 Example #2: Numeric Rewritten URLs 43 Example #3: Keyword-Rich Rewritten URLs 44 Maintaining URL Consistency 44 URL Rewriting 46 Installing mod_rewrite 48 Testing mod_rewrite 49 Introducing -
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ENISA Position Paper No. 2 Reputation-based Systems: a security analysis Editors: Elisabetta Carrara and Giles Hogben, ENISA October 2007 Reputation-based Systems ENISA Position Papers represent expert opinion on topics ENISA considers to be important emerging risks or key security components. They are produced as the result of discussion among a group of experts who were selected for their knowledge in the area. The content was collected via wiki, mailing list and telephone conferences and edited by ENISA. This paper aims to provide a useful introduction to security issues affecting Reputation-based Systems by identifying a number of possible threats and attacks, highlighting the security requirements that should be fulfilled by these systems and providing recommendations for action and best practices to reduce the security risks to users. Examples are given from a number of providers throughout the paper. These should be taken as examples only and there is no intention to single out a specific provider for criticism or praise. The examples provided are not necessarily those most representative or important, nor is the aim of this paper to conduct any kind of market survey, as there might be other providers which are not mentioned here and nonetheless are equally or more representative of the market. Audience This paper is aimed at providers, designers, research and standardisation communities, government policy-makers and businesses. ENISA Position Paper No.2 Reputation-based Systems: a security analysis 1 Reputation-based Systems EXECUTIVE -
The History of Digital Spam
The History of Digital Spam Emilio Ferrara University of Southern California Information Sciences Institute Marina Del Rey, CA [email protected] ACM Reference Format: This broad definition will allow me to track, in an inclusive Emilio Ferrara. 2019. The History of Digital Spam. In Communications of manner, the evolution of digital spam across its most popular appli- the ACM, August 2019, Vol. 62 No. 8, Pages 82-91. ACM, New York, NY, USA, cations, starting from spam emails to modern-days spam. For each 9 pages. https://doi.org/10.1145/3299768 highlighted application domain, I will dive deep to understand the nuances of different digital spam strategies, including their intents Spam!: that’s what Lorrie Faith Cranor and Brian LaMacchia ex- and catalysts and, from a technical standpoint, how they are carried claimed in the title of a popular call-to-action article that appeared out and how they can be detected. twenty years ago on Communications of the ACM [10]. And yet, Wikipedia provides an extensive list of domains of application: despite the tremendous efforts of the research community over the last two decades to mitigate this problem, the sense of urgency ``While the most widely recognized form of spam is email spam, the term is applied to similar abuses in other media: instant remains unchanged, as emerging technologies have brought new messaging spam, Usenet newsgroup spam, Web search engine spam, dangerous forms of digital spam under the spotlight. Furthermore, spam in blogs, wiki spam, online classified ads spam, mobile when spam is carried out with the intent to deceive or influence phone messaging spam, Internet forum spam, junk fax at scale, it can alter the very fabric of society and our behavior. -
Web Spam Taxonomy
Web Spam Taxonomy Zolt´an Gy¨ongyi Hector Garcia-Molina Computer Science Department Computer Science Department Stanford University Stanford University [email protected] [email protected] Abstract techniques, but as far as we know, they still lack a fully effective set of tools for combating it. We believe Web spamming refers to actions intended to mislead that the first step in combating spam is understanding search engines into ranking some pages higher than it, that is, analyzing the techniques the spammers use they deserve. Recently, the amount of web spam has in- to mislead search engines. A proper understanding of creased dramatically, leading to a degradation of search spamming can then guide the development of appro- results. This paper presents a comprehensive taxon- priate countermeasures. omy of current spamming techniques, which we believe To that end, in this paper we organize web spam- can help in developing appropriate countermeasures. ming techniques into a taxonomy that can provide a framework for combating spam. We also provide an overview of published statistics about web spam to un- 1 Introduction derline the magnitude of the problem. There have been brief discussions of spam in the sci- As more and more people rely on the wealth of informa- entific literature [3, 6, 12]. One can also find details for tion available online, increased exposure on the World several specific techniques on the Web itself (e.g., [11]). Wide Web may yield significant financial gains for in- Nevertheless, we believe that this paper offers the first dividuals or organizations. Most frequently, search en- comprehensive taxonomy of all important spamming gines are the entryways to the Web; that is why some techniques known to date. -
The Domain Abuse Activity Reporting (DAAR) System
The DAAR System The Domain Abuse Activity Reporting (DAAR) System David Piscitello, ICANN and Greg Aaron, iThreat Cyber Group Abstract Efforts to study domain name abuse are common today, but these often have one or more limitations. They may only use a statistically valid sampling of the domain name space or use limited sets of domain name abuse (reputation) data. They may concentrate only on a particular security threat, e.g., spam. Notably, few studies collect and retain data over a sufficiently long timeframe to provide opportunities for historical analysis. This paper describes our efforts to collect a large body of domain name registration data, and to complement these data with a large set of reputation data feeds that report and classify multiple security threats. We intend that the resulting data repository will serve as a platform for daily or historical analysis or reporting. Ideally, any findings or reporting derived from our system can be independently validated. We thus use publicly or commercially available data so that the reports or findings from any studies that use our system would be reproducible by any party who collects the same data sets and applies the same processing rules. The long- term objective for our project is that this system will serve the community by establishing a persistent, fact-based repository for ongoing collection, analysis, and reporting. Version 0.3, November 2017 1 The DAAR System Table of Contents INTRODUCTION AND BACKGROUND 3 PURPOSES OF THE DAAR PROJECT 4 DAAR OVERVIEW 4 DAAR COLLECTION SYSTEM 5 DAAR DATA COLLECTION 5 TOP-LEVEL DOMAIN ZONE DATA 5 DOMAIN NAME REGISTRATION DATA 7 DOMAIN REPUTATION DATA (ABUSE DATA) 8 DAAR REPORTING SYSTEM 8 SECURITY THREATS OBSERVED BY THE DAAR 9 DAAR THREAT DATA COMPILATION 12 REPUTATION DATA USED BY DAAR 13 SELECTION OF REPUTATION DATA 14 MULTIPLE REPUTATION DATA SOURCES 15 FALSE POSITIVE RATES 16 DOES DAAR CAPTURE ALL OF THE ABUSE? 16 DAAR REPORTING 18 ABUSE SCORING 19 ACCESS TO THE DAAR SYSTEM 20 CONCLUSION 20 ANNEX A. -
Spam in Blogs and Social Media
ȱȱȱȱ ȱ Pranam Kolari, Tim Finin Akshay Java, Anupam Joshi March 25, 2007 ȱ • Spam on the Internet –Variants – Social Media Spam • Reason behind Spam in Blogs • Detecting Spam Blogs • Trends and Issues • How can you help? • Conclusions Pranam Kolari is a UMBC PhD Tim Finin is a UMBC Professor student. His dissertation is on with over 30 years of experience spam blog detection, with tools in the applying AI to information developed in use both by academia and systems, intelligent interfaces and industry. He has active research interest robotics. Current interests include social in internal corporate blogs, the Semantic media, the Semantic Web and multi- Web and blog analytics. agent systems. Akshay Java is a UMBC PhD student. Anupam Joshi is a UMBC Pro- His dissertation is on identify- fessor with research interests in ing influence and opinions in the broad area of networked social media. His research interests computing and intelligent systems. He include blog analytics, information currently serves on the editorial board of retrieval, natural language processing the International Journal of the Semantic and the Semantic Web. Web and Information. Ƿ Ȭȱ • Early form seen around 1992 with MAKE MONEY FAST • 80-85% of all e-mail traffic is spam • In numbers 2005 - (June) 30 billion per day 2006 - (June) 55 billion per day 2006 - (December) 85 billion per day 2007 - (February) 90 billion per day Sources: IronPort, Wikipedia http://www.ironport.com/company/ironport_pr_2006-06-28.html ȱȱǵ • “Unsolicited usually commercial e-mail sent to a large -
Cloak and Dagger: Dynamics of Web Search Cloaking
Cloak and Dagger: Dynamics of Web Search Cloaking David Y. Wang, Stefan Savage, and Geoffrey M. Voelker Deptartment of Computer Science and Engineering University of California, San Diego ABSTRACT of markets, either via “organic” search results or sponsored search Cloaking is a common “bait-and-switch” technique used to hide the placements—together comprising a $14B marketing sector [16]. true nature of a Web site by delivering blatantly different semantic Not surprisingly, the underlying value opportunities have created content to different user segments. It is often used in search engine strong incentives to influence search results—a field called “search optimization (SEO) to obtain user traffic illegitimately for scams. engine optimization” or SEO. Some of these techniques are be- In this paper, we measure and characterize the prevalence of cloak- nign and even encouraged by search engine operators (e.g., simpli- ing on different search engines, how this behavior changes for tar- fying page content, optimizing load times, etc.) while others are geted versus untargeted advertising and ultimately the response to designed specifically to manipulate page ranking algorithms with- site cloaking by search engine providers. Using a custom crawler, out regard to customer interests (e.g., link farms, keyword stuffing, called Dagger, we track both popular search terms (e.g., as identi- blog spamming, etc.) Thus, a cat and mouse game has emerged fied by Google, Alexa and Twitter) and targeted keywords (focused between search engine operators and scammers where search oper- on pharmaceutical products) for over five months, identifying when ators try to identify and root out pages deemed to use “black hat” distinct results were provided to crawlers and browsers. -
Identifying Javascript Skimmers on High-Value Websites
Imperial College of Science, Technology and Medicine Department of Computing CO401 - Individual Project MEng Identifying JavaScript Skimmers on High-Value Websites Author: Supervisor: Thomas Bower Dr. Sergio Maffeis Second marker: Dr. Soteris Demetriou June 17, 2019 Identifying JavaScript Skimmers on High-Value Websites Thomas Bower Abstract JavaScript Skimmers are a new type of malware which operate by adding a small piece of code onto a legitimate website in order to exfiltrate private information such as credit card numbers to an attackers server, while also submitting the details to the legitimate site. They are impossible to detect just by looking at the web page since they operate entirely in the background of the normal page operation and display no obvious indicators to their presence. Skimmers entered the public eye in 2018 after a series of high-profile attacks on major retailers including British Airways, Newegg, and Ticketmaster, claiming the credit card details of hundreds of thousands of victims between them. To date, there has been little-to-no work towards preventing websites becoming infected with skimmers, and even less so for protecting consumers. In this document, we propose a novel and effective solution for protecting users from skimming attacks by blocking attempts to contact an attackers server with sensitive information, in the form of a Google Chrome web extension. Our extension takes a two-pronged approach, analysing both the dynamic behaviour of the script such as outgoing requests, as well as static analysis by way of a number of heuristic techniques on scripts loaded onto the page which may be indicative of a skimmer.