DOCSLIB.ORG

A Study of Spam Origins

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Study of Spam Origins Spamology: A Study of Spam Origins ∗ Craig A. Shue Minaxi Gupta Chin Hua Kong Oak Ridge National Computer Science Dept. Computer Science Dept. Laboratory Indiana University Indiana University Oak Ridge, Tennessee, USA Bloomington, Indiana, USA Bloomington, Indiana, USA [email protected] [email protected] [email protected] John T. Lubia Asim S. Yuksel Computer Science Dept. Computer Science Dept. Indiana University Indiana University Bloomington, Indiana, USA Bloomington, Indiana, USA [email protected] [email protected] ABSTRACT email lists for very little money.” This paper is motivated by The rise of spam in the last decade has been staggering, with the question: How are these bulk email lists created? While the rate of spam exceeding that of legitimate email. While previous work has explored one avenue for how spammers conjectures exist on how spammers gain access to email ad- might be harvesting email addresses [1], our goal is to un- dresses to spam, most work in the area of spam containment dertake a systematic study to understand the phenomenon. has either focused on better spam filtering methodologies Our general approach is to register a dedicated domain, or on understanding the botnets commonly used to send run an email server for that domain, and post email ad- spam. In this paper, we aim to understand the origins of dresses belonging to the domain at strategic places and watch spam. We post dedicated email addresses to record how the inflow of spam. The first step we take is to register for and where spammers go to obtain email addresses. We find free offers on the Web and give each web site a unique email that posting an email address on public web pages yields address. This is motivated by the simple observation that immediate and high-volume spam. Surprisingly, even sim- Internet users are frequently asked to trust and confide in ple email obfuscation approaches are still sufficient today a variety of web sites. Many sites offer their services, such to prevent spammers from harvesting emails. We also find as email accounts, and news for free. However, to access that attempts to find open relays continue to be popular these services, users must register and provide some per- among spammers. The insights we gain on the use of web sonal information. This often includes an email address, al- crawlers used to harvest email addresses and the common- lowing the site to contact the user. While users may implic- alities of techniques used by spammers open the door for itly assume that the site will securely and responsibly han- radically different follow-up work on spam containment and dle the confidential registration information, this assump- even systematic enforcement of spam legislation at a large tion may not hold in practice. Our next step is to post scale. unique, site-specific email addresses at popular sites with a blog or a comment section, such as washingtonpost.com and nytimes.com. The intent behind this step is to see if 1. INTRODUCTION the posted email addresses get discovered by web crawlers Spam exceeds legitimate email today. It wastes band- that eventually lead to spam. To compare the likelihood width and storage, delays valid emails, and hurts human pro- of getting email addresses harvested from popular sites ver- ductivity. Given these implications, significant efforts have sus less popular ones, and to understand the behavior of rightly been devoted to spam containment. While we have crawlers that lead to spam, we also post multiple email ad- made great leaps in developing novel spam-filtering tech- dresses on various web pages belonging to our department. niques and in understanding the botnets typically used to We exposed 22,230 unique email addresses and monitored send spam, our understanding of where spammers get the the inflow of spam for a period of almost 5 months. The re- email addresses to spam is largely confined to “they buy bulk sulting data helped us draw various interesting conclusions ∗ and also helped confirm some prevailing wisdom. Specifi- Craig Shue performed this work while a Ph.D. candidate at Indiana University. cally, we concluded that: Final edits of the submitted manuscript have been authored • by a contractor of the U.S. Government under contract DE- Users must exercise caution when divulging AC05-00OR22725. Accordingly, the U.S. Government re- their email addresses. Though none of the popular tains a nonexclusive, royalty-free license to publish or re- web sites where we registered for accounts spammed, produce the published form of this contribution, or allow a few of the less popular ones did send spam. Publicly others to do so, for U.S. Government purposes. posting email addresses at both popular and unpopu- lar sites led to the most spam. • Spam arrives instantly. Our first spam arrived in less than one hour, indicating that new email addresses get discovered quickly on the Internet. web sites in a manner that were available to web crawlers. Our general approach was to create a new email address to • Commonly-used email obfuscation techniques provide in each case. This allowed us to track the outcome are offering protection (for now). It is common on a per-site basis. In the passive approach, we simply ob- practice to replace the conventional @ in email ad- served the unsolicited spam sent to our mail server. We now dresses by an AT in order to defeat email harvesting. describe each in detail. We found that the spammers are still not parsing sim- ple obfuscations as of now. However, one should not 1. Signing up for mailing lists and newsletters: Our count on the protection offered by such simple obfus- first data set was motivated by the question “How often do cation schemes, for they are trivial to defeat. web sites leak email addresses provided to them?” To col- lect data to answer this question, we signed up and created • Spamming crawlers exist and can be tracked. accounts at 70 different types of web sites, including those Our scheme of dynamically presenting each new vis- that promise free offers and mailing lists and newsletters. itor to our department web pages with a new email We provided a unique email address belonging to our regis- address allowed us to track which crawlers, if any, led tered domain to each site in order to track which of these to spam. We found that crawling for harvesting email sites lead to spam. We used two different ways to find these address is occurring regularly. Thus, blocking access sites. First, we used the Alexa Web Information Service [3] for spamming crawlers could be an important new step to find popular web sites which allowed creation of accounts. in spam containment. This confirms findings from the 14 of the 70 accounts we created were in Alexa’s top 1000 work in Project Honey Pot [1]. web sites according to their traffic rankings. Second, we searched for pages that had forms that asked for email ad- • Top spammers use multiple email-harvesting dresses. This led to the creation of 56 additional accounts. strategies. Top spammers crawled popular and un- Combined, these 70 accounts helped us understand the be- popular web pages to harvest email addresses. They havior of popular web sites in comparison to the unpopular also made attempts to check if our mail server could be ones. We refer to this data set as the ACT (standing for used as an open relay. On the one hand, it shows the account) data set throughout the rest of this paper. aggressiveness of spammers. On the other, it opens up new avenues for fighting spam because anybody who 2. Posting emails on popular web sites: The second is doing both could be filtered. data set was motivated by the question “What is the likeli- hood of receiving spam on an email address posted at a pop- ular site?” To collect data to answer this question, we posted Roadmap: The remainder of this paper is organized as fol- 9 unique email addresses on 4 popular web sites. 3 of these lows: We explain our data collection technique in Section 2. sites were in the Alexa top 1000 popular sites. The email ad- In Section 3, we provide an overview of the spam we collect dresses were often added in comment sections of news stories and in Section 4, we analyze how quickly spam is received or in blogs. To avoid removal of the email addresses by site once an email address is exposed. Section 5 examines spam- moderators, yet still avoid legitimate emails, we included in- ming trends at the domain and IP granularity and Section 6 nocuous commentary along with each email address. Since investigates the behavior of web crawlers that are tied to our comments are unlikely to garner responses from regular spam campaigns. We review related work in Section 7 and visitors to these sites, we consider any email sent to these 9 discuss the implications of this study on spam containment addresses to be spam. Subsequently, this data set is denoted in Section 8. as the PST (standing for post) data set. 3. Posting static email addresses on less popular 2. DATA COLLECTION METHODOLOGY web pages: This data set was motivated by the ques- Spammers can harvest email addresses from multiple sour- tion: “What is the likelihood of receiving spam on an email ces on the Internet, including web pages, blogs, newsgroups, address at a less popular web site?” To collect this data social networking sites, and mailing lists. In our study, we set, we posted two different email addresses on two depart- examine the ramifications of making email addresses avail- mental course web pages.
Load more

Recommended publications
  • Trendmicro™ Hosted Email Security
    TrendMicro™ Hosted Email Security Best Practice Guide Trend Micro Incorporated reserves the right to make changes to this document and to the products described herein without notice. The names of companies, products, people, characters, and/or data mentioned herein are fictitious and are in no way intended to represent any real individual, company, product, or event, unless otherwise noted. Complying with all applicable copyright laws is the responsibility of the user. Copyright © 2016 Trend Micro Incorporated. All rights reserved. Trend Micro, the Trend Micro t-ball logo, and TrendLabs are trademarks or registered trademarks of Trend Micro, Incorporated. All other brand and product names may be trademarks or registered trademarks of their respective companies or organizations. No part of this publication may be reproduced, photocopied, stored in a retrieval system, or transmitted without the express prior written consent of Trend Micro Incorporated. Authors : Michael Mortiz, Jefferson Gonzaga Editorial : Jason Zhang Released : June 2016 Table of Contents 1 Best Practice Configurations ................................................................................................................................. 8 1.1 Activating a domain ....................................................................................................................................... 8 1.2 Adding Approved/Blocked Sender ................................................................................................................ 8 1.3 HES order
    [Show full text]
  • A Survey of Learning-Based Techniques of Email Spam Filtering
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Unitn-eprints Research A SURVEY OF LEARNING-BASED TECHNIQUES OF EMAIL SPAM FILTERING Enrico Blanzieri and Anton Bryl January 2008 (Updated version) Technical Report # DIT-06-056 A Survey of Learning-Based Techniques of Email Spam Filtering Enrico Blanzieri, University of Trento, Italy, and Anton Bryl University of Trento, Italy, Create-Net, Italy [email protected] January 11, 2008 Abstract vertising pornography, pyramid schemes, etc. [68]. The total worldwide financial losses caused by spam Email spam is one of the major problems of the to- in 2005 were estimated by Ferris Research Analyzer day’s Internet, bringing financial damage to compa- Information Service at $50 billion [31]. nies and annoying individual users. Among the ap- Lately, Goodman et al. [39] presented an overview proaches developed to stop spam, filtering is an im- of the field of anti-spam protection, giving a brief portant and popular one. In this paper we give an history of spam and anti-spam and describing major overview of the state of the art of machine learn- directions of development. They are quite optimistic ing applications for spam filtering, and of the ways in their conclusions, indicating learning-based spam of evaluation and comparison of different filtering recognition, together with anti-spoofing technologies methods. We also provide a brief description of and economic approaches, as one of the measures other branches of anti-spam protection and discuss which together will probably lead to the final victory the use of various approaches in commercial and non- over email spammers in the near future.
    [Show full text]
  • Detecting Phishing in Emails Srikanth Palla and Ram Dantu, University of North Texas, Denton, TX
    1 Detecting Phishing in Emails Srikanth Palla and Ram Dantu, University of North Texas, Denton, TX announcements. The third category of spammers is called Abstract — Phishing attackers misrepresent the true sender and phishers. Phishing attackers misrepresent the true sender and steal consumers' personal identity data and financial account steal the consumers' personal identity data and financial credentials. Though phishers try to counterfeit the websites in account credentials. These spammers send spoofed emails and the content, they do not have access to all the fields in the lead consumers to counterfeit websites designed to trick email header. Our classification method is based on the recipients into divulging financial data such as credit card information provided in the email header (rather than the numbers, account usernames, passwords and social security content of the email). We believe the phisher cannot modify numbers. By hijacking brand names of banks, e-retailers and the complete header, though he can forge certain fields. We credit card companies, phishers often convince the recipients based our classification on three kinds of analyses on the to respond. Legislation can not help since a majority number header: DNS-based header analysis, Social Network analysis of phishers do not belong to the United States. In this paper we and Wantedness analysis. In the DNS-based header analysis, present a new method for recognizing phishing attacks so that we classified the corpus into 8 buckets and used social the consumers can be vigilant and not fall prey to these network analysis to further reduce the false positives. We counterfeit websites. Based on the relation between credibility introduced a concept of wantedness and credibility, and and phishing frequency, we classify the phishers into i) derived equations to calculate the wantedness values of the Prospective Phishers ii) Suspects iii) Recent Phishers iv) Serial email senders.
    [Show full text]
  • Efficient Spam Filtering System Based on Smart Cooperative Subjective and Objective Methods*
    Int. J. Communications, Network and System Sciences, 2013, 6, 88-99 http://dx.doi.org/10.4236/ijcns.2013.62011 Published Online February 2013 (http://www.scirp.org/journal/ijcns) Efficient Spam Filtering System Based on Smart * Cooperative Subjective and Objective Methods Samir A. Elsagheer Mohamed1,2 1College of Computer, Qassim University, Qassim, KSA 2Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt Email: [email protected], [email protected] Received September 17, 2012; revised January 16, 2013; accepted January 25, 2013 ABSTRACT Most of the spam filtering techniques are based on objective methods such as the content filtering and DNS/reverse DNS checks. Recently, some cooperative subjective spam filtering techniques are proposed. Objective methods suffer from the false positive and false negative classification. Objective methods based on the content filtering are time con- suming and resource demanding. They are inaccurate and require continuous update to cope with newly invented spammer’s tricks. On the other side, the existing subjective proposals have some drawbacks like the attacks from mali- cious users that make them unreliable and the privacy. In this paper, we propose an efficient spam filtering system that is based on a smart cooperative subjective technique for content filtering in addition to the fastest and the most reliable non-content-based objective methods. The system combines several applications. The first is a web-based system that we have developed based on the proposed technique. A server application having extra features suitable for the enter- prises and closed work groups is a second part of the system. Another part is a set of standard web services that allow any existing email server or email client to interact with the system.
    [Show full text]
  • Account Administrator's Guide
    ePrism Email Security Account Administrator’s Guide - V10.4 4225 Executive Sq, Ste 1600 Give us a call: Send us an email: For more info, visit us at: La Jolla, CA 92037-1487 1-800-782-3762 [email protected] www.edgewave.com © 2001—2016 EdgeWave. All rights reserved. The EdgeWave logo is a trademark of EdgeWave Inc. All other trademarks and registered trademarks are hereby acknowledged. Microsoft and Windows are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. Other product and company names mentioned herein may be the trademarks of their respective owners. The Email Security software and its documentation are copyrighted materials. Law prohibits making unauthorized copies. No part of this software or documentation may be reproduced, transmitted, transcribed, stored in a retrieval system, or translated into another language without prior permission of EdgeWave. 10.4 Contents Chapter 1 Overview 1 Overview of Services 1 Email Filtering (EMF) 2 Archive 3 Continuity 3 Encryption 4 Data Loss Protection (DLP) 4 Personal Health Information 4 Personal Financial Information 5 Document Conventions 6 Other Conventions 6 Supported Browsers 7 Reporting Spam to EdgeWave 7 Contacting Us 7 Additional Resources 7 Chapter 2 Portal Overview 8 Navigation Tree 9 Work Area 10 Navigation Icons 10 Getting Started 11 Logging into the portal for the first time 11 Logging into the portal after registration 12 Changing Your Personal Information 12 Configuring Accounts 12 Chapter 3 EdgeWave Administrator
    [Show full text]
  • IFIP AICT 394, Pp
    A Scalable Spam Filtering Architecture Nuno Ferreira1, Gracinda Carvalho1, and Paulo Rogério Pereira2 1 Universidade Aberta, Portugal 2 INESC-ID, Instituto Superior Técnico, Technical University of Lisbon, Portugal [email protected], [email protected], [email protected] Abstract. The proposed spam filtering architecture for MTA1 servers is a component based architecture that allows distributed processing and centralized knowledge. This architecture allows heterogeneous systems to coexist and benefit from a centralized knowledge source and filtering rules. MTA servers in the infrastructure contribute to a common knowledge, allowing for a more rational resource usage. The architecture is fully scalable, ranging from all-in- one system with minimal components instances, to multiple components instances distributed across multiple systems. Filtering rules can be implemented as independent modules that can be added, removed or modified without impact on MTA servers operation. A proof-of-concept solution was developed. Most of spam is filtered due to a grey-listing effect from the architecture itself. Using simple filters as Domain Name System black and white lists, and Sender Policy Framework validation, it is possible to guarantee a spam filtering effective, efficient and virtually without false positives. Keywords: spam filtering, distributed architecture, component based, centralized knowledge, heterogeneous system, scalable deployment, dynamic rules, modular implementation. 1 Introduction Internet mail spam2 is a problem for most organizations and individuals. Receiving spam on mobile devices, and on other connected appliances, is yet a bigger problem, as these platforms are not the most appropriate for spam filtering. Spam can be seen as belonging to one of two major categories: Fraud and Commercial.
    [Show full text]
  • White Paper Barracuda • Comprehensive Email Filtering
    Comprehensive Email Filtering White Paper Barracuda • Comprehensive Email Filtering Email has undoubtedly become a valued communications tool among organizations worldwide. With Spam History frequent virus attacks and the alarming influx of spam, email loses the efficiency to communicate. Spam Spam is one form of abuse messages represent the vast majority of email traffic on the Internet. Spam is no longer a simple annoyance; of the Simple Mail Transfer it is a significant security issue and a massive drain on financial resources. Protocol (SMTP), which is implemented in email systems This is simply astounding and unacceptable. It’s time to take control of spam and virus attacks. on the basis of RFC 524. First proposed in 1973, RFC 524 was Filtering Email in Two Advanced Processes developed during a time when computer security was not a Today, there are a number of solutions designed to help alleviate the spam problem. Barracuda Networks significant concern. As such, designed the affordable Barracuda Email Security Gateway as an easy-to-use, enterprise-class hardware and RFC 524 is no longer a secure software solution for businesses of all sizes that comprehensively evaluates each email, using two main command set, making it and classes of sophisticated algorithms and techniques: Connection Management and Mail Scanning. SMTP susceptible to abuse. During the connection management process, emails are filtered through five defense layers to verify Most spam-making tools authenticity of envelope information, and any inappropriate incoming mail connections are dropped exploit the security holes in even before receiving the message. Any emails that survive the connection verification process must then SMTP.
    [Show full text]
  • Email Filtering for Providers
    Email Filtering for providers How email service providers and ISPs can use anti-phishing, anti-ransomware and graymail filtering as a strategic weapon. Win new customers, keep your current ones, and avoid being stuck in commodity pricing hell. Table of Contents Summary .............................................................................................. 3 Introduction ......................................................................................... 4 What is an ISP or Host To Do? ....................................................... 5 The Spectrum of Email Threats and Nuisances ........................ 5 Spam ................................................................................................... 6 Graymail .............................................................................................. 7 Phishing and Spear Phishing ............................................................... 8 Malware through Phishing and Spear Phishing Campaigns ................10 Stopping Spear Phishing is Difficult ...................................................11 Hard Costs of Email Threats and Nuisances ............................ 11 Opportunity Costs of Email Security .......................................... 12 Mitigating the Full Spectrum of Email Threats .......................... 12 Heuristic Filtering and Rules to Fight Graymail and Spam ..................13 Anti-Phishing/Spear Phishing ............................................................14 Anti-Malware and Attachment Analysis .............................................15
    [Show full text]
  • System Administrator's Guide
    ePrism Email Security System Administrator’s Guide - V11.0 4225 Executive Sq, Ste 1600 Give us a call: Send us an email: For more info, visit us at: La Jolla, CA 92037-1487 1-800-782-3762 [email protected] www.edgewave.com © 2001—2017 EdgeWave. All rights reserved. The EdgeWave logo is a trademark of EdgeWave Inc. All other trademarks and registered trademarks are hereby acknowledged. Microsoft and Windows are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. Other product and company names mentioned herein may be the trademarks of their respective owners. The Email Security software and its documentation are copyrighted materials. Law prohibits making unauthorized copies. No part of this software or documentation may be reproduced, transmitted, transcribed, stored in a retrieval system, or translated into another language without prior permission of EdgeWave. 11.0 Contents Chapter 1 Overview 1 Overview of Services 1 Email Filtering (EMF) 2 Archive 3 Continuity 3 Encryption 4 Data Loss Protection (DLP) 4 Personal Health Information 4 Personal Financial Information 5 ThreatCheck 6 Vx Service 6 Document Conventions 7 Supported Browsers 7 Reporting Spam to EdgeWave 7 Contacting Us 8 Additional Resources 8 Chapter 2 ePrism Appliance 9 Planning for the ePrism Appliance 9 About MX Records 9 Configuration Examples 10 Email Security Outside Corporate Firewall 10 Email Security Behind Corporate Firewall 10 Mandatory 11 Optional 12 Accessing the ePrism Appliance 12 ePrism Appliance
    [Show full text]
  • A Survey of Phishing Email Filtering Techniques Ammar Almomani, B
    2070 IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 15, NO. 4, FOURTH QUARTER 2013 A Survey of Phishing Email Filtering Techniques Ammar Almomani, B. B. Gupta, Samer Atawneh, A. Meulenberg, and Eman Almomani Abstract—Phishing email is one of the major problems of messages and are capable of deceiving even the clever Internet today’s Internet, resulting in financial losses for organizations users. Fraudulent emails can steal secret information from and annoying individual users. Numerous approaches have been the victims, resulting in loss of funds. As a consequence, developed to filter phishing emails, yet the problem still lacks a complete solution. In this paper, we present a survey of these attacks are damaging electronic commerce in the Internet the state of the art research on such attacks. This is the first world, resulting in the loss of trust and use of the Internet [9]. comprehensive survey to discuss methods of protection against This threat has led to the development of a large number of phishing email attacks in detail. We present an overview of the techniques for the detection and filtering of phishing emails. various techniques presently used to detect phishing email, at the The many approaches proposed in the literature to filter different stages of attack, mostly focusing on machine-learning techniques. A comparative study and evaluation of these filtering phishing emails, may be classified according to the different methods is carried out. This provides an understanding of the stages of the attack flow, e.g. network level protection, au- problem, its current solution space, and the future research thentication, client side tool, user education, server side filters directions anticipated.
    [Show full text]
  • Download File
    Scaling up VoIP: Transport Protocols and Controlling Unwanted Communication Requests Kumiko Ono Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2012 c 2012 Kumiko Ono All Rights Reserved ABSTRACT Scaling up VoIP: Transport Protocols and Controlling Unwanted Communication Requests Kumiko Ono Millions of people worldwide use voice over IP (VoIP) services not only as cost-effective alternatives to long distance and international calls but also as unified communication tools, such as video conferencing. Owing to the low cost of new user accounts, each person can easily obtain multiple accounts for various purposes. Rich VoIP functions combined with the low cost of new accounts and connections attract many people, resulting in a dramatic increase in the number of active user accounts. Internet telephony service providers (ITSPs), therefore, need to deploy VoIP systems to accommodate this growing demand for VoIP user accounts. Attracted people also include bad actors who make calls that are unwanted to callees. Once ITSPs openly connect with each other, unwanted bulk calls will be at least as serious a problem as email spam. This dissertation studies how we can reduce load both on ITSPs and end users to ensure continuing the success of VoIP services. From ITSPs' perspective, the scalability of VoIP servers is of importance and concern. Scalability depends on server implementation and the transport protocol for SIP, VoIP sig- naling. We conduct experiments to understand the impact of connection-oriented transport protocols, namely, TCP and SCTP, because of the additional costs of handling connec- tions.
    [Show full text]
  • Bypassing Phishing Protections with Email Authentication
    Master Security and Network Engineering Bypassing phishing protections with email authentication Adrien Raulot [email protected] February 4, 2019 MSc Security and Network Engineering Supervisors: Marat Nigmatullin, Alex Stavroulakis Abstract Over the past years, phishing has been an increasing threat to end users and organizations. Spam filters form the first line of defense against phishers, aiming at preventing spoofed emails from reaching the end user's inbox. The present study is designed to assess the effectiveness of these filters against phishing emails and determine whether techniques exist that may be used to avoid detection. In order to determine what network and authentication aspects of phishing emails allow for detection, a subset of phishing emails has been analysed using two popular open source spam filters. After experimenting with the spam filters, being compliant has proven to be an effective method to bypass detection. In many cases, sending authenticated phishing emails using a newly registered domain appears to be sufficient in order to avoid detection and allow phishing emails to reach the end user's inbox. The analysis of this method on some popular email service providers reveals that no effective protections against this method appear to be in place. Finally, some mitigation measures that may be used to protect the end user against such attacks are discussed and recommendations for future work are given. 2 Contents 1 Introduction 4 1.1 Theoretical framework . .5 1.1.1 Phishing email characteristics . .5 1.1.2 Phishing email detection techniques . .5 2 Related work 6 3 Methodology 7 4 Results 8 4.1 Email processing with spam filters .
    [Show full text]