Machine Learning for E-Mail Spam Filtering: Review, Techniques and Trends
Total Page:16
File Type:pdf, Size:1020Kb
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 -
DMARC — Defeating E-Mail Abuse
CERT-EU Security Whitepaper 17-001 DMARC — Defeating E-Mail Abuse Christos Koutroumpas ver. 1.3 February 9, 2017 TLP: WHITE 1 Preface E-mail is one of the most valuable and broadly used means of communication and most orga- nizations strongly depend on it. The Simple Mail Transport Protocol (SMTP) – the Internet’s underlying email protocol – was adopted in the eighties and is still in use after 35 years. When it was designed, the need for security was not so obvious, and therefore security was not incor- porated in the design of this protocol. As a result, the protocol is susceptible to a wide range of attacks. Spear-phishing campaigns in particular can be more successful by spoofing (altering) the originator e-mail address to imper- sonate a trusted or trustworthy organization or person. This can lead to luring the recipient into giving away credentials or infecting his/her computer by executing malware delivered through the e-mail. While raising user awareness on how to avoid e-mail fraud is recommended, the Verizon Data Breach Investigations Report indicates that more needs to be done. The DBIR report reveals that 30% of all phishing e-mail messages were opened by the recipients and with 12% clicked on the content and executed malicious code. The median time for the first user of a phishing campaign to open the malicious email is 1 minute, 40 seconds. The median time to the first click on the attachment was 3 minutes, 45 seconds. These statistics highlight the risk for an organization on the receiving end of spear-phishing e-mails. -
Delivering Results to the Inbox Sailthru’S 2020 Playbook on Deliverability, Why It’S Imperative and How It Drives Business Results Introduction to Deliverability
Delivering Results to the Inbox Sailthru’s 2020 Playbook on Deliverability, Why It’s Imperative and How It Drives Business Results Introduction to Deliverability Every day, people receive more than 293 billion Deliverability is the unsung hero of email marketing, emails, a staggering number that only represents ultimately ensuring a company’s emails reach their the tip of the iceberg. Why? The actual number intended recipients. It’s determined by a host of of emails sent is closer to 5.9 quadrillion, with the factors, including the engagement of your subscribers overwhelming majority blocked outright or delivered and the quality of your lists. All together, these factors to the spam folder. result in your sender reputation score, which is used to determine how the ISPs treat your email stream. Something many people don’t realize is that to the Deliverability is also a background player, so far in the major Internet Service Providers (ISPs) — Gmail, shadows that many people don’t think about it, until Yahoo!, Hotmail, Comcast and AOL — “spam” there’s a major issue. doesn’t refer to marketing messages people may find annoying, but rather malicious email filled with That’s why Sailthru’s deliverability team created this scams and viruses. In order to protect their networks guide. Read on to learn more about how deliverability and their customers, the ISPs cast a wide net. If a works on the back-end and how it impacts revenue, message is deemed to be spam by the ISP’s filters, it’s your sender reputation and how to maintain a good dead on arrival, never to see the light of the inbox, as one, and best practices for list management, email protecting users’ inboxes is the top priority of any ISP. -
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. -
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. -
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. -
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. -
Webfaction User Guide
WebFaction User Guide WebFaction is a service of Paragon Internet Group Limited CONTENTS 1 Introduction 3 1.1 Services..................................................3 1.2 The Complete System..........................................4 2 The Control Panel 5 2.1 Log in to the Control Panel.......................................5 2.2 Change Your Control Panel Password..................................5 2.3 What to Do About a Lost Password...................................6 2.4 Two-Step Login.............................................6 3 Finding Details About Your Server9 3.1 Finding Your Server’s Name.......................................9 3.2 Finding Your Server’s Operating System................................9 3.3 Finding Your Server’s IP Address.................................... 10 4 Accessing Your Data 11 4.1 Connecting with SSH.......................................... 11 4.2 Connecting with FTP........................................... 14 4.3 Changing Your FTP or SSH Password.................................. 14 4.4 Additional Users............................................. 15 4.5 Backups................................................. 16 5 Accounts 17 5.1 Plans and Services............................................ 17 5.2 Communicating with WebFaction.................................... 18 5.3 Payments................................................. 19 5.4 Affiliate Program............................................. 23 5.5 Canceling Your Account......................................... 24 6 Domains 25 6.1 Getting -
Combatting Spam Using Mimedefang, Spamassassin and Perl
Combating Spam Using SpamAssassin, MIMEDefang and Perl Copyright 2003 David F. Skoll Roaring Penguin Software Inc. (Booth #23) Administrivia Please turn off or silence cell phones, pagers, Blackberry devices, etc... After the tutorial, please be sure to fill out an evaluation form and return it to the USENIX folks. 2 Overview After this tutorial, you will: Understand how central mail filtering works. Know how to use MIMEDefang to filter mail. Be able to integrate SpamAssassin into your mail filter. Know how to implement mail filtering policies with MIMEDefang and Perl. Know how to fight common spammer tactics. 3 Outline Introduction to Mail Filtering Sendmail's Milter API MIMEDefang Introduction, Architecture Writing MIMEDefang Filters SpamAssassin Integration Advanced Filter Writing Fighting Common Spammer Tactics Advanced Topics Policy Suggestions 4 Assumptions I assume that you: Are familiar with Sendmail configuration. You don't need to be a sendmail.cf guru, but should know the basics. Are familiar with Perl. Again, you don't need to be able to write an AI program in a Perl one- liner, but should be able to read simple Perl scripts. Are running the latest version of Sendmail 8.12 on a modern UNIX or UNIX-like system. 5 Why Filter Mail? The old reason: to stop viruses. The new reason: to stop spam and inappropriate content. Blocking viruses is easy. Block .exe and similar files, and test against signature databases. Blocking spam is hard, but becoming increasingly important. Organizations can even face lawsuits over inappropriate content. 6 Mail filtering is required for many reasons. In addition to the reasons given on the slide, you might need to filter outgoing mail as well to prevent virus propagation, dissemination of sensitive information, etc. -
Set up Mail Server Documentation 1.0
Set Up Mail Server Documentation 1.0 Nosy 2014 01 23 Contents 1 1 1.1......................................................1 1.2......................................................2 2 11 3 13 3.1...................................................... 13 3.2...................................................... 13 3.3...................................................... 13 4 15 5 17 5.1...................................................... 17 5.2...................................................... 17 5.3...................................................... 17 5.4...................................................... 18 6 19 6.1...................................................... 19 6.2...................................................... 28 6.3...................................................... 32 6.4 Webmail................................................. 36 6.5...................................................... 37 6.6...................................................... 38 7 39 7.1...................................................... 39 7.2 SQL.................................................... 41 8 43 8.1...................................................... 43 8.2 strategy.................................................. 43 8.3...................................................... 44 8.4...................................................... 45 8.5...................................................... 45 8.6 Telnet................................................... 46 8.7 Can postfix receive?.......................................... -
Busting Blocks: Revisiting 47 U.S.C. §230 to Address the Lack of Effective Legal Recourse for Wrongful Inclusion in Spam Filters
Digital Commons @ Touro Law Center Scholarly Works Faculty Scholarship 2011 Busting Blocks: Revisiting 47 U.S.C. §230 to Address the Lack of Effective Legal Recourse for Wrongful Inclusion in Spam Filters Jonathan I. Ezor Touro Law Center, [email protected] Follow this and additional works at: https://digitalcommons.tourolaw.edu/scholarlyworks Part of the Commercial Law Commons, Communications Law Commons, and the Science and Technology Law Commons Recommended Citation Jonathan I. Ezor, Busting Blocks: Revisiting 47 U.S.C. § 230 to Address the Lack of Effective Legal Recourse for Wrongful Inclusion in Spam Filters, XVII Rich. J.L. & Tech. 7 (2011), http://jolt.richmond.edu/ v17i2/article7.pdf. This Article is brought to you for free and open access by the Faculty Scholarship at Digital Commons @ Touro Law Center. It has been accepted for inclusion in Scholarly Works by an authorized administrator of Digital Commons @ Touro Law Center. For more information, please contact [email protected]. Richmond Journal of Law and Technology Vol. XVII, Issue 2 BUSTING BLOCKS: REVISITING 47 U.S.C. § 230 TO ADDRESS THE LACK OF EFFECTIVE LEGAL RECOURSE FOR WRONGFUL INCLUSION IN SPAM FILTERS By Jonathan I. Ezor∗ Cite as: Jonathan I. Ezor, Busting Blocks: Revisiting 47 U.S.C. § 230 to Address the Lack of Effective Legal Recourse for Wrongful Inclusion in Spam Filters, XVII Rich. J.L. & Tech. 7 (2011), http://jolt.richmond.edu/v17i2/article7.pdf. I. INTRODUCTION: E-MAIL, BLOCK LISTS, AND THE LAW [1] Consider a company that uses e-mail to conduct a majority of its business, including customer and vendor communication, marketing, and filing official documents. -
Macsysadmin 2009 Presentatio
Slide 1 SpamAssassin Way more than the Mac OS X Server GUI shows Presented by: Kevin A. McGrail Project Management Committee Member of the Apache Software Foundation SpamAssassin Project & President, PCCC September 16, 2009 Good Afternoon, My name is Kevin A. McGrail. If you read my biographyi for this conference, you’ll know already that I hate Spam and enjoy greatly fighting spammers. You’ll also know that I love all types of computers and use a wide variety of machines & operating systems. But I’m definitely old-school in my love for the command line interface. This doesn’t mean I don’t think that Apple’s OS X is the most beautiful pairing of a rock-solid CLI with a beautifully polished GUI. But it does mean that while we are here to talk about Mac system administration, the configuration of SpamAssassin is largely not server specific and most of the heavy- handed configuration changes will be done behind the scenes using the CLI. So let’s get started by talking about the definition of Spam. Page 1 of 67 Slide 2 What is Spam? •Spam is NOT about content, its about CONSENT. – Consent: to give assent or approval : agree <consent to being tested> Merriam‐Webster Dictionary •What is SPAM vs. spam? September 16, 2009 Chris Santerre gave the best definition of Spam I’ve ever seen. He based the definition of Spam on CONSENT not content because consent is when you give approval to someone to send you e-mails. Many people try and use various legal definitions such as CAN-SPAM in the US.