Asian Anti-Spam Guide 1
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Prospects, Leads, and Subscribers
PAGE 2 YOU SHOULD READ THIS eBOOK IF: You are looking for ideas on finding leads. Spider Trainers can help You are looking for ideas on converting leads to Marketing automation has been shown to increase subscribers. qualified leads for businesses by as much as 451%. As You want to improve your deliverability. experts in drip and nurture marketing, Spider Trainers You want to better maintain your lists. is chosen by companies to amplify lead and demand generation while setting standards for design, You want to minimize your list attrition. development, and deployment. Our publications are designed to help you get started, and while we may be guilty of giving too much information, we know that the empowered and informed client is the successful client. We hope this white paper does that for you. We look forward to learning more about your needs. Please contact us at 651 702 3793 or [email protected] . ©2013 SPIDER TRAINERS PAGE 3 TAble Of cOnTenTS HOW TO cAPTure SubScriberS ...............................2 HOW TO uSe PAiD PrOGrAMS TO GAin Tipping point ..................................................................2 SubScriberS ...........................................................29 create e mail lists ...........................................................3 buy lists .........................................................................29 Pop-up forms .........................................................4 rent lists ........................................................................31 negative consent -
A Rule Based Approach for Spam Detection
A RULE BASED APPROACH FOR SPAM DETECTION Thesis submitted in partial fulfillment of the requirements for the award of degree of Master of Engineering In Computer Science & Engineering By: Ravinder Kamboj (Roll No. 800832030) Under the supervision of: Dr. V.P Singh Mrs. Sanmeet Bhatia Assistant Professor Assistant Professor Computer Science & Engineering Department of SMCA COMPUTER SCIENCE AND ENGINEERING DEPARTMENT THAPAR UNIVERSITY PATIALA – 147004 JULY- 2010 i ii Abstract Spam is defined as a junk Email or unsolicited Email. Spam has increased tremendously in the last few years. Today more than 85% of e-mails that are received by e-mail users are spam. The cost of spam can be measured in lost human time, lost server time and loss of valuable mail. Spammers use various techniques like spam via botnet, localization of spam and image spam. According to the mail delivery process anti-spam measures for Email Spam can be divided in to two parts, based on Emails envelop and Email data. Black listing, grey listing and white listing techniques can be applied on the Email envelop to detect spam. Techniques based on the data part of Email like heuristic techniques and Statistical techniques can be used to combat spam. Bayesian filters as part of statistical technique divides the income message in to words called tokens and checks their probability of occurrence in spam e-mails and ham e-mails. Two types of approaches can be followed for the detection of spam e-mails one is learning approach other is rule based approach. Learning approach required a large dataset of spam e-mails and ham e-mails is required for the training of spam filter; this approach has good time characteristics filter can be retrained quickly for new Spam. -
A Survey on Spam Detection Techniques
ISSN (Online) : 2278-1021 ISSN (Print) : 2319-5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 12, December 2014 A survey on spam detection techniques Anjali Sharma1, Manisha 2, Dr.Manisha 3 , Dr.Rekha Jain 4 1,2,3,4 Bansthali Vidyapith, Jaipur Campus, India Abstract: Today e-mails have become one of the most popular and economical forms of communication for Internet users. Thus due to its popularity, the e-mail is going to be misused. One such misuse is the posting of unwelcome, unwanted e-mails known as spam or junk e-mails [1]. E-mail spam has various consequences. It reduces productivity, takes extra space in mail boxes, extra time, extend software damaging viruses, and materials that contains potentially harmful information for Internet users, destroy stability of mail servers, and as a result users spend lots of time for sorting incoming mail and deleting unwanted correspondence. So there is a need of spam detection so that its consequences can be reduced [2]. In this paper, we present various spam detection techniques. Keywords: Spam, Spam detection techniques, Email classification I. INTRODUCTION Spam refers to unsolicited commercial email. Also known firewalls; therefore, it is an especially useful way for as junk mail, spam floods Internet users’ electronic spammers. It targets the users when they join any chat mailboxes. These junk mails can contain various types of room to find new friends. It spoils enjoy of people and messages such as pornography, commercial advertising, waste their time also. doubtful product, viruses or quasi legal services [3]. -
Presentations Made by Senders
SES ���� ��� � �� � � � � � � � ������������� DomainKeys ��������� SPF ��������������������� ���������� ����������������� ������������������������������������������������ Contents Introduction 3 Deployment: For Email Receivers 6 Audience 3 Two Sides of the Coin 6 How to Read this White Paper 3 Recording Trusted Senders Who Passed Authentication 6 A Vision for Spam-Free Email 4 Whitelisting Incoming Forwarders 6 The Problem of Abuse 4 What To Do About Forgeries 6 The Underlying Concept 4 Deployment: For ISPs and Enterprises 7 Drivers; or, Who’s Buying It 4 Complementary considerations for ISPs 7 Vision Walkthrough 5 Deployment: For MTA vendors 8 About Sender Authentication 8 Which specification? 8 An Example 8 Conformance testing 8 History 8 Perform SRS and prepend headers when forwarding 8 How IP-based Authentication Works 9 Add ESMTP support for Submitter 8 The SPF record 9 Record authentication and policy results in the headers 8 How SPF Classic Works 9 Join the developers mailing list 8 How Sender ID works 9 Deployment: For MUA vendors 9 How Cryptographic Techniques Work 0 Displaying Authentication-Results 9 Using Multiple Approaches Automatic switching to port 587 9 Reputation Systems Deployment: For ESPs 20 Deployment: For Email Senders 2 Don’t look like a phisher! 20 First, prepare. 2 Delegation 20 Audit Your Outbound Mailstreams 2 Publish Appropriately 20 Construct the record 2 Deployment: For Spammers 2 Think briefly about PRA and Mail-From contexts. 3 Two Types of Spammers 2 Test the record, part 3 Publish SPF and sign with DomainKeys. 2 Put the record in DNS 3 Stop forging random domains. 2 Test the record, part 2 4 Buy your own domains. 2 Keep Track of Violations 4 Reuse an expired domain. -
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. -
Taxonomy of Email Reputation Systems (Invited Paper)
Taxonomy of Email Reputation Systems (Invited Paper) Dmitri Alperovitch, Paul Judge, and Sven Krasser Secure Computing Corporation 4800 North Point Pkwy Suite 400 Alpharetta, GA 30022 678-969-9399 {dalperovitch, pjudge, skrasser}@securecomputing.com Abstract strong incentive for people to act maliciously without paying reputational consequences [1]. While this Today a common goal in the area of email security problem can be solved by disallowing anonymity on is to provide protection from a wide variety of threats the Internet, email reputation systems are able to by being more predictive instead of reactive and to address this problem in a much more practical fashion. identify legitimate messages in addition to illegitimate By assigning a reputation to every email entity, messages. There has been previous work in the area of reputation systems can influence agents to operate email reputation systems that can accomplish these responsibly for fear of getting a bad reputation and broader goals by collecting, analyzing, and being unable to correspond with others [2]. distributing email entities' past behavior The goal of an email reputation system is to monitor characteristics. In this paper, we provide taxonomy activity and assign a reputation to an entity based on its that examines the required properties of email past behavior. The reputation value should be able to reputation systems, identifies the range of approaches, denote different levels of trustworthiness on the and surveys previous work. spectrum from good to bad. In 2000, Resnick et al. described Internet reputation system as having three 1. Introduction required properties [3]: • Entities are long lived, As spam volumes have continued to increase with • feedback about current interactions is high rates, comprising 90% of all email by the end of captured and distributed, and 2006 as determined by Secure Computing Research, • past feedback guides buyer decisions. -
Design of SMS Commanded-And-Controlled and P2P-Structured Mobile Botnets
Design of SMS Commanded-and-Controlled and P2P-Structured Mobile Botnets Yuanyuan Zeng, Kang G. Shin, Xin Hu The University of Michigan, Ann Arbor, MI 48109-2121, U.S.A. fgracez, kgshin, [email protected] Abstract—Botnets have become one of the most serious security is usually capable of only one or two functions. Although the threats to the Internet and personal computer (PC) users. number of mobile malware families and their variants has been Although botnets have not yet caused major outbreaks in mobile growing steadily over the recent years, their functionalities networks, with the rapidly-growing popularity of smartphones such as Apple’s iPhone and Android-based phones that store have remained simple until recently. more personal data and gain more capabilities than earlier- SymbOS.Exy.A trojan [2] was discovered in February 2009 generation handsets, botnets are expected to move towards this and its variant SymbOS.Exy.C resurfaced in July 2009. This mobile domain. Since SMS is ubiquitous to every phone and can mobile worm, which is said to have “botnet-esque” behavior delay message delivery for offline phones, it is a suitable medium patterns, differs from other mobile malware because after for command and control (C&C). In this paper, we describe how a mobile botnet can be built by utilizing SMS messages infection, it connects back to a malicious HTTP server and for C&C, and how different P2P structures can be exploited reports information of the device and its user. The Ikee.B for mobile botnets. Our simulation results demonstrate that a worm [3] targets jailbroken iPhones, and has behavior similar modified Kademlia—a structured architecture—is a better choice to SymbOS.Exy. -
WHITE PAPER Email Deliverability Review
WHITE PAPER Email DELIVeraBility REView dmawe are the White Paper Email Deliverability Review Published by Deliverability Hub of the Email Marketing Council Sponsored by 1 COPYRIGHT: THE DIRECT MARKETING ASSOCIATION (UK) LTD 2012 WHITE PAPER Email DELIVeraBility REView Contents About this document ...............................................................................................................................3 About the authors ...................................................................................................................................4 Sponsor’s perspective .............................................................................................................................5 Executive summary .................................................................................................................................6 1. Major factors that impact on deliverability ..............................................................................................7 1.1 Sender reputation .............................................................................................................................7 1.2 Spam filtering ...................................................................................................................................7 1.3 Blacklist operators ............................................................................................................................8 1.4 Smart Inboxes ..................................................................................................................................9 -
Anti-Spam Methods
INTRODUCTION Spamming is flooding the Internet with many copies of the same message, in an attempt to force the message on people who would not otherwise choose to receive it. Most spam is commercial advertising, often for dubious products, get-rich-quick schemes, or quasi- legal services. Spam costs the sender very little to send -- most of the costs are paid for by the recipient or the carriers rather than by the sender. There are two main types of spam, and they have different effects on Internet users. Cancellable Usenet spam is a single message sent to 20 or more Usenet newsgroups. (Through long experience, Usenet users have found that any message posted to so many newsgroups is often not relevant to most or all of them.) Usenet spam is aimed at "lurkers", people who read newsgroups but rarely or never post and give their address away. Usenet spam robs users of the utility of the newsgroups by overwhelming them with a barrage of advertising or other irrelevant posts. Furthermore, Usenet spam subverts the ability of system administrators and owners to manage the topics they accept on their systems. Email spam targets individual users with direct mail messages. Email spam lists are often created by scanning Usenet postings, stealing Internet mailing lists, or searching the Web for addresses. Email spams typically cost users money out-of-pocket to receive. Many people - anyone with measured phone service - read or receive their mail while the meter is running, so to speak. Spam costs them additional money. On top of that, it costs money for ISPs and online services to transmit spam, and these costs are transmitted directly to subscribers. -
Spam (Spam 2.0) Through Web Usage
Digital Ecosystems and Business Intelligence Institute Addressing the New Generation of Spam (Spam 2.0) Through Web Usage Models Pedram Hayati This thesis is presented for the Degree of Doctor of Philosophy of Curtin University July 2011 I Abstract Abstract New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications. The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method. This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering. This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. -
Statistics Survey 357528 'Survey on Internet Attitudes'
Quick statistics Survey 357528 'Survey on Internet Attitudes' Results Survey 357528 Number of records in this query: 1000 Total records in survey: 1000 Percentage of total: 100.00% page 1 / 226 Quick statistics Survey 357528 'Survey on Internet Attitudes' Field summary for QA1 I agree to these terms and want to participate in the survey. I confirm that I am 16 years of age or over. Answer Count Percentage Yes (A1) 1000 100.00% No answer 0 0.00% page 2 / 226 Quick statistics Survey 357528 'Survey on Internet Attitudes' Field summary for QB1a(SQ001) How often do you go online and for which of the following activities (for private purposes)? [a. Check my email] Answer Count Percentage Several times a day (A1) 893 89.30% Daily (A2) 92 9.20% Weekly (A3) 10 1.00% Monthly (A4) 2 0.20% Less than monthly (A5) 0 0.00% Never (A6) 3 0.30% No answer 0 0.00% page 3 / 226 Quick statistics Survey 357528 'Survey on Internet Attitudes' Field summary for QB1a(SQ002) How often do you go online and for which of the following activities (for private purposes)? [b. Use instant messaging (e.g. WhatsApp, Facebook Messenger)] Answer Count Percentage Several times a day (A1) 577 57.70% Daily (A2) 165 16.50% Weekly (A3) 87 8.70% Monthly (A4) 28 2.80% Less than monthly (A5) 33 3.30% Never (A6) 110 11.00% No answer 0 0.00% page 4 / 226 Quick statistics Survey 357528 'Survey on Internet Attitudes' Field summary for QB1a(SQ003) How often do you go online and for which of the following activities (for private purposes)? [c. -
84. Blog Spam Detection Using Intelligent Bayesian Approach
International Journal of Engineering Research and General Science Volume 2, Issue 5, August-September, 2014 ISSN 2091-2730 Blog-Spam Detection Using intelligent Bayesian Approach - Krushna Pandit, Savyasaachi Pandit Assistant Professor, GCET, VVNagar, E-mail- [email protected] , M - 9426759947 Abstract Blog-spam is one of the major problems of the Internet nowadays. Since the history of the internet the spam are considered a huge threat to the security and reliability of web content. The spam is the unsolicited messages sent for the fulfillment of the sender’s purpose and to harm the privacy of user, site owner and/or to steal available resource over the internet (may be or may not be allocated to).For dealing with spam there are so many methodologies available. Nowadays the blog spamming is a rising threat to safety, reliability, & purity of the published internet content. Since the search engines are using certain specific algorithms for creating the searching page-index/rank for the websites (i.e. google-analytics), it has attracted so many attention to spam the SMS(Social Media Sites) for gaining rank in order to increase the company’s popularity. The available solutions to malicious content detection are quite a more to be used very frequently in order to fulfill the requirement of analyzing all the web content in certain time with least possible ―false positives‖. For this purpose a site level algorithm is needed so that it can be easy, cheap & understandable (for site modifiers) to filter and monitor the content being published. Now for that we use a ―Bayes Theorem‖ of the ―Statistical approach‖.