IJESRT INTERNATIONAL JOURNAL of ENGINEERING SCIENCES & RESEARCH TECHNOLOGY a REVIEW: TYPE SPECIFICATION BASED VIDEO SPAM DETECTION Pooja Kamboj*, Er
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Copyright 2009 Cengage Learning. All Rights Reserved. May Not Be Copied, Scanned, Or Duplicated, in Whole Or in Part
Index Note: Page numbers referencing fi gures are italicized and followed by an “f ”. Page numbers referencing tables are italicized and followed by a “t”. A Ajax, 353 bankruptcy, 4, 9f About.com, 350 Alexa.com, 42, 78 banner advertising, 7f, 316, 368 AboutUs.org, 186, 190–192 Alta Vista, 7 Barack Obama’s online store, 328f Access application, 349 Amazon.com, 7f, 14f, 48, 247, BaseballNooz.com, 98–100 account managers, 37–38 248f–249f, 319–320, 322 BBC News, 3 ActionScript, 353–356 anonymity, 16 Bebo, 89t Adobe Flash AOL, 8f, 14f, 77, 79f, 416 behavioral changes, 16 application, 340–341 Apple iTunes, 13f–14f benign disinhibition, 16 fi le format. See .fl v fi le format Apple site, 11f, 284f Best Dates Now blog, 123–125 player, 150, 153, 156 applets, Java, 352, 356 billboard advertising, 369 Adobe GoLive, 343 applications, see names of specifi c bitmaps, 290, 292, 340, 357 Adobe Photoshop, 339–340 applications BJ’s site, 318 Advanced Research Projects ARPA (Advanced Research Black Friday, 48 Agency (ARPA), 2 Projects Agency), 2 blog communities, 8f advertising artistic fonts, 237 blog editors, 120, 142 dating sites, 106 ASCO Power University, 168–170 blog search engines, 126 defi ned, 397 .asf fi le format, 154t–155t Blogger, 344–347 e-commerce, 316 AskPatty.com, 206–209 blogging, 7f, 77–78, 86, 122–129, Facebook, 94–96 AuctionWeb, 7f 133–141, 190, 415 family and lifestyle sites, 109 audience, capturing and retaining, blogosphere, 122, 142 media, 373–376 61–62, 166, 263, 405–407, blogrolls, 121, 142 message, 371–372 410–422, 432 Blue Nile site, -
Liferay Portal 6 Enterprise Intranets
Liferay Portal 6 Enterprise Intranets Build and maintain impressive corporate intranets with Liferay Jonas X. Yuan BIRMINGHAM - MUMBAI Liferay Portal 6 Enterprise Intranets Copyright © 2010 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: April 2010 Production Reference: 1230410 Published by Packt Publishing Ltd. 32 Lincoln Road Olton Birmingham, B27 6PA, UK. ISBN 978-1-849510-38-7 www.packtpub.com Cover Image by Karl Swedberg ([email protected]) Credits Author Editorial Team Leader Jonas X. Yuan Aanchal Kumar Reviewer Project Team Leader Amine Bousta Lata Basantani Acquisition Editor Project Coordinator Dilip Venkatesh Shubhanjan Chatterjee Development Editor Proofreaders Mehul Shetty Aaron Nash Lesley Harrison Technical Editors Aditya Belpathak Graphics Alfred John Geetanjali Sawant Charumathi Sankaran Nilesh Mohite Copy Editors Production Coordinators Leonard D'Silva Avinish Kumar Sanchari Mukherjee Aparna Bhagat Nilesh Mohite Indexers Hemangini Bari Cover Work Rekha Nair Aparna Bhagat About the Author Dr. -
Address Munging: the Practice of Disguising, Or Munging, an E-Mail Address to Prevent It Being Automatically Collected and Used
Address Munging: the practice of disguising, or munging, an e-mail address to prevent it being automatically collected and used as a target for people and organizations that send unsolicited bulk e-mail address. Adware: or advertising-supported software is any software package which automatically plays, displays, or downloads advertising material to a computer after the software is installed on it or while the application is being used. Some types of adware are also spyware and can be classified as privacy-invasive software. Adware is software designed to force pre-chosen ads to display on your system. Some adware is designed to be malicious and will pop up ads with such speed and frequency that they seem to be taking over everything, slowing down your system and tying up all of your system resources. When adware is coupled with spyware, it can be a frustrating ride, to say the least. Backdoor: in a computer system (or cryptosystem or algorithm) is a method of bypassing normal authentication, securing remote access to a computer, obtaining access to plaintext, and so on, while attempting to remain undetected. The backdoor may take the form of an installed program (e.g., Back Orifice), or could be a modification to an existing program or hardware device. A back door is a point of entry that circumvents normal security and can be used by a cracker to access a network or computer system. Usually back doors are created by system developers as shortcuts to speed access through security during the development stage and then are overlooked and never properly removed during final implementation. -
Adversarial Web Search by Carlos Castillo and Brian D
Foundations and TrendsR in Information Retrieval Vol. 4, No. 5 (2010) 377–486 c 2011 C. Castillo and B. D. Davison DOI: 10.1561/1500000021 Adversarial Web Search By Carlos Castillo and Brian D. Davison Contents 1 Introduction 379 1.1 Search Engine Spam 380 1.2 Activists, Marketers, Optimizers, and Spammers 381 1.3 The Battleground for Search Engine Rankings 383 1.4 Previous Surveys and Taxonomies 384 1.5 This Survey 385 2 Overview of Search Engine Spam Detection 387 2.1 Editorial Assessment of Spam 387 2.2 Feature Extraction 390 2.3 Learning Schemes 394 2.4 Evaluation 397 2.5 Conclusions 400 3 Dealing with Content Spam and Plagiarized Content 401 3.1 Background 402 3.2 Types of Content Spamming 405 3.3 Content Spam Detection Methods 405 3.4 Malicious Mirroring and Near-Duplicates 408 3.5 Cloaking and Redirection 409 3.6 E-mail Spam Detection 413 3.7 Conclusions 413 4 Curbing Nepotistic Linking 415 4.1 Link-Based Ranking 416 4.2 Link Bombs 418 4.3 Link Farms 419 4.4 Link Farm Detection 421 4.5 Beyond Detection 424 4.6 Combining Links and Text 426 4.7 Conclusions 429 5 Propagating Trust and Distrust 430 5.1 Trust as a Directed Graph 430 5.2 Positive and Negative Trust 432 5.3 Propagating Trust: TrustRank and Variants 433 5.4 Propagating Distrust: BadRank and Variants 434 5.5 Considering In-Links as well as Out-Links 436 5.6 Considering Authorship as well as Contents 436 5.7 Propagating Trust in Other Settings 437 5.8 Utilizing Trust 438 5.9 Conclusions 438 6 Detecting Spam in Usage Data 439 6.1 Usage Analysis for Ranking 440 6.2 Spamming Usage Signals 441 6.3 Usage Analysis to Detect Spam 444 6.4 Conclusions 446 7 Fighting Spam in User-Generated Content 447 7.1 User-Generated Content Platforms 448 7.2 Splogs 449 7.3 Publicly-Writable Pages 451 7.4 Social Networks and Social Media Sites 455 7.5 Conclusions 459 8 Discussion 460 8.1 The (Ongoing) Struggle Between Search Engines and Spammers 460 8.2 Outlook 463 8.3 Research Resources 464 8.4 Conclusions 467 Acknowledgments 468 References 469 Foundations and TrendsR in Information Retrieval Vol. -
Efficient and Trustworthy Review/Opinion Spam Detection
International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 5 Issue: 4 86 – 94 _____________________________________________________________________________________________ Efficient and Trustworthy Review/Opinion Spam Detection Sanketi P. Raut Prof. Chitra Wasnik ME II (Computer), Computer Dept. Lokmanya Tilak College of Engineering, Lokmanya Tilak College of Engineering, New Mumbai, New Mumbai, Email:[email protected] Email:[email protected] Abstract - The most common mode for consumers to express their level of satisfaction with their purchases is through online ratings, which we can refer as Online Review System. Network analysis has recently gained a lot of attention because of the arrival and the increasing attractiveness of social sites, such as blogs, social networking applications, micro blogging, or customer review sites. The reviews are used by potential customers to find opinions of existing users before purchasing the products. Online review systems plays an important part in affecting consumers' actions and decision making, and therefore attracting many spammers to insert fake feedback or reviews in order to manipulate review content and ratings. Malicious users misuse the review website and post untrustworthy, low quality, or sometimes fake opinions, which are referred as Spam Reviews. In this study, we aim at providing an efficient method to identify spam reviews and to filter out the spam content with the dataset of gsmarena.com. Experiments on the dataset collected from gsmarena.com show that the proposed system achieves higher accuracy than the standard naïve bayes. Keywords - Review Spam detection, Opinion, Text mining, WordNet, naïve bayes classifier, logistic Regression. __________________________________________________*****_________________________________________________ making purchase. Opinions/Reviews are middle of almost all I. -
Battling the Internet Water Army: Detection of Hidden Paid Posters
Battling the Internet Water Army: Detection of Hidden Paid Posters Cheng Chen Kui Wu Venkatesh Srinivasan Xudong Zhang Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science Dept. of Computer Science University of Victoria University of Victoria University of Victoria Peking University Victoria, BC, Canada Victoria, BC, Canada Victoria, BC, Canada Beijing, China Abstract—We initiate a systematic study to help distinguish on different online communities and websites. Companies are a special group of online users, called hidden paid posters, or always interested in effective strategies to attract public atten- termed “Internet water army” in China, from the legitimate tion towards their products. The idea of online paid posters ones. On the Internet, the paid posters represent a new type of online job opportunity. They get paid for posting comments is similar to word-of-mouth advertisement. If a company hires and new threads or articles on different online communities enough online users, it would be able to create hot and trending and websites for some hidden purposes, e.g., to influence the topics designed to gain popularity. Furthermore, the articles opinion of other people towards certain social events or business or comments from a group of paid posters are also likely markets. Though an interesting strategy in business marketing, to capture the attention of common users and influence their paid posters may create a significant negative effect on the online communities, since the information from paid posters is usually decision. In this way, online paid posters present a powerful not trustworthy. When two competitive companies hire paid and efficient strategy for companies. -
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. -
Image Spam Detection: Problem and Existing Solution
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 02 | Feb 2019 www.irjet.net p-ISSN: 2395-0072 Image Spam Detection: Problem and Existing Solution Anis Ismail1, Shadi Khawandi2, Firas Abdallah3 1,2,3Faculty of Technology, Lebanese University, Lebanon ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Today very important means of communication messaging spam, Internet forum spam, junk fax is the e-mail that allows people all over the world to transmissions, and file sharing network spam [1]. People communicate, share data, and perform business. Yet there is who create electronic spam are called spammers [2]. nothing worse than an inbox full of spam; i.e., information The generally accepted version for source of spam is that it crafted to be delivered to a large number of recipients against their wishes. In this paper, we present a numerous anti-spam comes from the Monty Python song, "Spam spam spam spam, methods and solutions that have been proposed and deployed, spam spam spam spam, lovely spam, wonderful spam…" Like but they are not effective because most mail servers rely on the song, spam is an endless repetition of worthless text. blacklists and rules engine leaving a big part on the user to Another thought maintains that it comes from the computer identify the spam, while others rely on filters that might carry group lab at the University of Southern California who gave high false positive rate. it the name because it has many of the same characteristics as the lunchmeat Spam that is nobody wants it or ever asks Key Words: E-mail, Spam, anti-spam, mail server, filter. -
History of Spam
1 Max W. Mosing1 THE UPS AND DOWNS IN THE HISTORY OF EU-SPAM-REGULATIONS AND THEIR PRACTICAL IMPACT “Two years from now, spam will be solved. I promise a spam-free world by 2006” (Bill Gates; January 2004) Table of content: 1. (Legal) History of Spam................................................................................................................................................................................... 1 1.1. „Monty Python“and „Canter and Seigel“............................................................................................................................................ 2 1.2. Definition of Spam from a Legal Perspective ...................................................................................................................................... 2 1.3. Harm of Spam and– if the Internet community wants to ban spam – why is spam so successful?........................................... 3 1.4. Legal Framework and legal background of spam .............................................................................................................................. 4 2. “Spam-Regulations” in EU-Directives .......................................................................................................................................................... 6 2.1. Directive 97/66/EC concerning the processing of personal data and the protection of privacy in the telecommunications sector – ISDN-Directive and National Laws.............................................................................................................................................. -
Understanding the Business Ofonline Pharmaceutical Affiliate Programs
PharmaLeaks: Understanding the Business of Online Pharmaceutical Affiliate Programs Damon McCoy Andreas Pitsillidis∗ Grant Jordan∗ Nicholas Weaver∗† Christian Kreibich∗† Brian Krebs‡ Geoffrey M. Voelker∗ Stefan Savage∗ Kirill Levchenko∗ Department of Computer Science ∗Department of Computer Science and Engineering George Mason University University of California, San Diego †International Computer Science Institute ‡KrebsOnSecurity.com Berkeley, CA Abstract driven by competition between criminal organizations), a broad corpus of ground truth data has become avail- Online sales of counterfeit or unauthorized products able. In particular, in this paper we analyze the content drive a robust underground advertising industry that in- and implications of low-level databases and transactional cludes email spam, “black hat” search engine optimiza- metadata describing years of activity at the GlavMed, tion, forum abuse and so on. Virtually everyone has en- SpamIt and RX-Promotion pharmaceutical affiliate pro- countered enticements to purchase drugs, prescription- grams. By examining hundreds of thousands of orders, free, from an online “Canadian Pharmacy.” However, comprising a settled revenue totaling over US$170M, even though such sites are clearly economically moti- we are able to provide comprehensive documentation on vated, the shape of the underlying business enterprise three key aspects of underground advertising activity: is not well understood precisely because it is “under- Customers. We provide detailed analysis on the con- ground.” In this paper we exploit a rare opportunity to sumer demand for Internet-advertised counterfeit phar- view three such organizations—the GlavMed, SpamIt maceuticals, covering customer demographics, product and RX-Promotion pharmaceutical affiliate programs— selection (including an examination of drug abuse as a from the inside. -
Internet Security
In the News Articles in the news from the past month • “Security shockers: 75% of US bank websites Internet Security have flaws” • “Blank robbers swipe 3,000 ‘fraud-proof’ UK passports” • “Korean load sharks feed on hacked data” • “Worms spread via spam on Facebook and Nan Niu ([email protected]) MySpace” CSC309 -- Fall 2008 • “Beloved websites riddled with crimeware” • “Google gives GMail always-on encryption” http://www.theregister.co.uk 2 New Targets of 2007 Scenario 1 • Cyber criminals and cyber spies have • The Chief Information Security Officer shifted their focus again of a medium sized, but sensitive, federal – Facing real improvements in system and agency learned that his computer was network security sending data to computers in China. • The attackers now have two new targets • He had been the victim of a new type of spear phishing attack highlighted in this – users who are easily misled year’s Top 20. – custom-built applications • Once they got inside, the attackers had • Next, 4 exploits scenarios… freedom of action to use his personal • Reported by SANS (SysAdmin, Audit, Network, computer as a tunnel into his agency’s Security), http://www.sans.org systems. 3 4 Scenario 2 Scenario 3 • Hundreds of senior federal officials and business • A hospital’s website was compromised executives visited a political think-tank website that had been infected and caused their computers to because a Web developer made a become zombies. programming error. • Keystroke loggers, placed on their computers by the • Sensitive patient records were taken. criminals (or nation-state), captured their user names and passwords when their stock trading accounts and • When the criminals proved they had the their employers computers, and sent the data to data, the hospital had to choose between computers in different countries. -
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