DOCSLIB.ORG

A Review of Current and Next Generation Spam Filtering Tools

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Review of Current and Next Generation Spam Filtering Tools Tightening the net: a review of current and next generation spam filtering tools James Carpinter & Ray Hunt∗ Department of Computer Science and Software Engineering University of Canterbury Abstract IT infrastructure worldwide. While it is dif- ficult to quantify the level of spam currently This paper provides an overview of cur- sent, many reports suggest it represents sub- rent and potential future spam filtering ap- stantially more than half of all email sent and proaches. We examine the problems spam in- predict further growth for the foreseeable fu- troduces, what spam is and how we can mea- ture [18, 43, 30]. sure it. The paper primarily focuses on auto- For some, spam represents a minor irritant; mated, non-interactive filters, with a broad for others, a major threat to productivity. Ac- review ranging from commercial implemen- cording to a recent study by Stanford Univer- tations to ideas confined to current research sity [36], the average Internet user loses ten papers. Both machine learning and non- working days each year dealing with incoming machine learning based filters are reviewed as spam. Costs beyond those incurred sorting potential solutions and a taxonomy of known legitimate email from spam are also present: approaches presented. While a range of dif- 15% of all email contains some type of virus ferent techniques have and continue to be payload, and one in 3,418 emails contained evaluated in academic research, heuristic and pornographic images particularly harmful to Bayesian filtering dominate commercial filter- minors [54]. It is difficult to estimate the ulti- ing systems; therefore, a case study of these mate dollar cost of such expenses; however, techniques is presented to demonstrate and most estimates place the worldwide cost of evaluate the effectiveness of these popular spam in 2005, in terms of lost productivity techniques. and IT infrastructure investment, to be well Keywords: spam, ham, heuristics, over US$10 billion [29, 52]. machine learning, non-machine learning, The magnitude of the problem has intro- Bayesian filtering, blacklisting. duced a new dimension to the use of email: the spam filter. Such systems can be expen- 1 Introduction sive to deploy and maintain, placing a further strain on IT budgets. While the reduced flow The first message recognised as spam was sent of spam email into a user’s inbox is gener- to the users of Arpanet in 1978 and repre- ally welcomed, the existence of false positives sented little more than an annoyance. Today, often necessitates the user manually double- email is a fundamental tool for business com- checking filtered messages; this reality some- munication and modern life, and spam repre- what counteracts the assistance the filter de- sents a serious threat to user productivity and livers. The effectiveness of spam filters to im- prove user productivity is ultimately limited ∗email: [email protected] by the extent to which users must manually 1 review filtered messages for false positives. of current research. Section 4 details the eval- Unfortunately, the underlying business uation of spam filters, including a case study model of bulk emailers (spammers) is simply of the PreciseMail Anti-Spam system operat- too attractive. Commissions to spammers of ing at the University of Canterbury. Section 25–50% on products sold are not unusual [30]. 5 finishes the paper with some conclusions on On a collection of 200 million email addresses, the state of this research area. a response rate of 0.001% would yield a spam- mer a return of $25,000, given a $50 product. 1.1 Definition Any solution to this problem must reduce the profitability of the underlying business model; Spam is briefly defined by the TREC 2005 by either substantially reducing the number of Spam Track as “unsolicited, unwanted email emails reaching valid recipients, or increasing that was sent indiscriminately, directly or in- the expenses faced by the spammer. directly, by a sender having no current rela- Regrettably, no solution has yet been found tionship with the recipient” [12]. The key el- to this vexing problem. The classification task ements of this definition are expanded on in is complex and constantly changing. Con- a more extensive definition provided by Mail structing a single model to classify the broad Abuse Prevention Systems [35], which spec- range of spam types is difficult; this task ifies three requirements for a message to be is made near impossible with the realisation classified as spam. Firstly, the message must that spam types are constantly moving and be equally applicable to many other potential evolving. Furthermore, most users find false recipients (i.e. the identity of the recipient positives unacceptable. The active evolution and the context of the message is irrelevant). of spam can be partially attributed to chang- Secondly, the recipient has not granted ‘delib- ing tastes and trends in the marketplace; how- erated, explicit and still-revocable permission ever, spammers often actively tailor their mes- for it to be sent’. Finally, the communica- sages to avoid detection, adding a further im- tion of the message gives a ‘disproportionate pediment to accurate detection. benefit’ to the sender, as solely determined by The similarities between junk postal mail the recipient. Critically, they note that sim- and spam can be immediately recognised; ple personalisation does not make the identity however, the nature of the Internet has al- of the sender relevant and that failure by the lowed spam to grow uncontrollably. Spam user to explicitly opt-out during a registration can be sent with no cost to the sender: the process does not form consent. economic realities that regulate junk postal Both these definitions identify the predomi- mail do not apply to the internet. Further- nant characteristic of spam email: that a user more, the legal remedies that can be taken receives unsolicited email that has been sent against spammers are limited: it is not diffi- without any concern for their identity. cult to avoid leaving a trace, and spammers easily operate outside the jurisdiction of those 1.2 Solution strategies countries with anti-spam legislation. The remainder of this section provides sup- Proposed solutions to spam can be separated porting material on the topic of spam. Sec- into three broad categories: legislation, pro- tion 2 provides an overview of spam classifi- tocol change and filtering. cation techniques. Sections 3.1 and 3.2 pro- A number of governments have enacted leg- vide a more detailed discussion of some of the islation prohibiting the sending of spam email, spam filtering techniques known: given the including the USA (Can Spam Act 2004) rapidly evolving nature of this field, it should and the EU (directive 2002/58/EC). Ameri- be considered a snapshot of the critical areas can legislation requires an ‘opt-out’ list that 2 bulk mailers are required to provide; this # spam correctly classified is arguably less effective than the European SR = (and Australian) approach of requiring ex- Total # of spam messages plicit ‘opt-in’ requests from consumers want- # spam correctly classified ing to receive such emails. At present, legisla- SP = Total # of messages classified as spam tion has appeared to have little effect on spam volumes, with some arguing that the law has 2 × SP × SR F1 = contributed to an increase in spam by giving SP + SR bulk advertisers permission to send spam, as # email correctly classified A = long as certain rules were followed. Total # of emails Many proposals to change the way in which we send email have been put forward, includ- Figure 1: Common experimental measures for ing the required authentication of all senders, the evaluation of spam filters. a per email charge and a method of encap- sulating policy within the email address [28]. Such proposals, while often providing a near fications; however, no human element is re- complete solution, generally fail to gain sup- quired during the initial classification deci- port given the scope of a major upgrade or sion. Such systems represent the most com- replacement of existing email protocols. mon solution to resolving the spam problem, Interactive filters, often referred to as precisely because of their capacity to execute ‘challenge-response’ (C/R) systems, intercept their task without supervision and without re- incoming emails from unknown senders or quiring a fundamental change in underlying those suspected of being spam. These mes- email protocols. sages are held by the recipient’s email server, which issues a simple challenge to the sender 1.3 Statistical evaluation to establish that the email came from a hu- man sender rather than a bulk mailer. The Common experimental measures include underlying belief is that spammers will be un- spam recall (SR), spam precision (SP), F1 and interested in completing the ‘challenge’ given accuracy (A) (see figure 1 for formal defini- the huge volume of messages they sent; fur- tions of these measures). Spam recall is ef- thermore, if a fake email address is used by fectively spam accuracy. A legitimate email the sender, they will not receive the chal- classified as spam is considered to be a ‘false lenge. Selective C/R systems issue a challenge positive’; conversely, a spam message classi- only when the (non-interactive) spam filter is fied as legitimate is considered to be a ‘false unable to determine the class of a message. negative’. Challenge-response systems do slow down the The accuracy measure, while often quoted delivery of messages, and many people refuse by product vendors, is generally not useful to use the system1. when evaluating anti-spam solutions. The Non-interactive filters classify emails with- level of misclassifications (1 − A) consists out human interaction (such as that seen in of both false positives and false negatives; C/R systems). Such filters often permit user clearly a 99% accuracy rate with 1% false neg- interaction with the filter to customise user- atives (and no false positives) is preferable to specific options or to correct filter misclassi- the same level of accuracy with 1% false pos- itives (and no false negatives).
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]
  • Show Me the Money: Characterizing Spam-Advertised Revenue
    Show Me the Money: Characterizing Spam-advertised Revenue Chris Kanich∗ Nicholas Weavery Damon McCoy∗ Tristan Halvorson∗ Christian Kreibichy Kirill Levchenko∗ Vern Paxsonyz Geoffrey M. Voelker∗ Stefan Savage∗ ∗ y Department of Computer Science and Engineering International Computer Science Institute University of California, San Diego Berkeley, CA z Computer Science Division University of California, Berkeley Abstract money at all [6]. This situation has the potential to distort Modern spam is ultimately driven by product sales: policy and investment decisions that are otherwise driven goods purchased by customers online. However, while by intuition rather than evidence. this model is easy to state in the abstract, our under- In this paper we make two contributions to improving standing of the concrete business environment—how this state of affairs using measurement-based methods to many orders, of what kind, from which customers, for estimate: how much—is poor at best. This situation is unsurpris- ing since such sellers typically operate under question- • Order volume. We describe a general technique— able legal footing, with “ground truth” data rarely avail- purchase pair—for estimating the number of orders able to the public. However, absent quantifiable empiri- received (and hence revenue) via on-line store order cal data, “guesstimates” operate unchecked and can dis- numbering. We use this approach to establish rough, tort both policy making and our choice of appropri- but well-founded, monthly order volume estimates ate interventions. In this paper, we describe two infer- for many of the leading “affiliate programs” selling ence techniques for peering inside the business opera- counterfeit pharmaceuticals and software. tions of spam-advertised enterprises: purchase pair and • Purchasing behavior.
    [Show full text]
  • Zambia and Spam
    ZAMNET COMMUNICATION SYSTEMS LTD (ZAMBIA) Spam – The Zambian Experience Submission to ITU WSIS Thematic meeting on countering Spam By: Annabel S Kangombe – Maseko June 2004 Table of Contents 1.0 Introduction 1 1.1 What is spam? 1 1.2 The nature of Spam 1 1.3 Statistics 2 2.0 Technical view 4 2.1 Main Sources of Spam 4 2.1.1 Harvesting 4 2.1.2 Dictionary Attacks 4 2.1.3 Open Relays 4 2.1.4 Email databases 4 2.1.5 Inadequacies in the SMTP protocol 4 2.2 Effects of Spam 5 2.3 The fight against spam 5 2.3.1 Blacklists 6 2.3.2 White lists 6 2.3.3 Dial‐up Lists (DUL) 6 2.3.4 Spam filtering programs 6 2.4 Challenges of fighting spam 7 3.0 Legal Framework 9 3.1 Laws against spam in Zambia 9 3.2 International Regulations or Laws 9 3.2.1 US State Laws 9 3.2.2 The USA’s CAN‐SPAM Act 10 4.0 The Way forward 11 4.1 A global effort 11 4.2 Collaboration between ISPs 11 4.3 Strengthening Anti‐spam regulation 11 4.4 User education 11 4.5 Source authentication 12 4.6 Rewriting the Internet Mail Exchange protocol 12 1.0 Introduction I get to the office in the morning, walk to my desk and switch on the computer. One of the first things I do after checking the status of the network devices is to check my email.
    [Show full text]
  • Email Phishing for IT Providers How Phishing Emails Have Changed and How to Protect Your IT Clients
    Email Phishing for IT Providers How phishing emails have changed and how to protect your IT clients 1 © 2016 Calyptix Security Corporation. All rights reserved. I [email protected] I (800) 650 – 8930 (800) 650-8930 I [email protected] Contents Introduction ............................................................................................ 2 Phishing overview .................................................................................. 3 Trends in phishing emails ...................................................................... 6 Email phishing tactics .......................................................................... 11 Steps for MSP & VARS .......................................................................... 24 Advice for your clients .......................................................................... 29 Sources .................................................................................................. 35 1 © 2016 Calyptix Security Corporation. All rights reserved. I [email protected] I (800) 650 – 8930 Introduction There are only so many ways to break into a bank. You can march through the door. You can climb through a window. You can tunnel through the floor. There is the service entrance, the employee entrance, and access on the roof. Criminals who want to rob a bank will probably use an open route – such as a side door. It’s easier than breaking down a wall. Criminals who want to break into your network face a similar challenge. They need to enter. They can look for a weakness in your
    [Show full text]
  • CUB Guide to Fighting Robocalls
    CitizensUtilityBoard.org 1-800-669-5556 Guide to Fighting Robocalls February 2021 The latest news on the robocall fi ght Robocalls are prerecorded messages from computer-generat- ed dialers, and Illinois is one of the nation’s hardest hit states. In early 2021, for example, the state received more than 153 million robocalls (about 57 per second) in the span of just one month. That ranked Illinois eighth in the country for these calls, according to the robocall-blocking fi rm YouMail. While there are helpful robocalls (alerting you to school closings or when a prescription is ready), YouMail estimates about 42 percent of the calls in Illinois were scams and another 22 percent were simply marketing pitches. Unwanted robocalls are annoying, and costly. The Federal Com- munications Commission (FCC) put the price tag at $3 billion a year just from lost time, not even counting any fraud. TechRe- One in 10 Americans public put the total annual loss for consumers at $9.5 billion. are scammed each year, While policymakers are fi nally starting to act against illegal robo- calls, don’t wait for federal law to catch up. Use the simple tips in resulting in an annual loss of this guide to protect yourself from unwanted calls. The law The Telephone Robocall Abuse Criminal Enforcement and $9.5 billion Deterrence (TRACED) Act became federal law in 2019. The act increases penalties and requires phone companies to validate Source: TechRepublic, calls before they reach you. This is to combat “spoofi ng,” October 2019 when a robocaller uses your area code and/or prefi x to ap- pear as if someone locally—maybe a friend or neighbor—is Note: In December 2020, the FCC ordered that organiza- trying to reach you.
    [Show full text]
  • Locating Spambots on the Internet
    BOTMAGNIFIER: Locating Spambots on the Internet Gianluca Stringhinix, Thorsten Holzz, Brett Stone-Grossx, Christopher Kruegelx, and Giovanni Vignax xUniversity of California, Santa Barbara z Ruhr-University Bochum fgianluca,bstone,chris,[email protected] [email protected] Abstract the world-wide email traffic [20], and a lucrative busi- Unsolicited bulk email (spam) is used by cyber- ness has emerged around them [12]. The content of spam criminals to lure users into scams and to spread mal- emails lures users into scams, promises to sell cheap ware infections. Most of these unwanted messages are goods and pharmaceutical products, and spreads mali- sent by spam botnets, which are networks of compro- cious software by distributing links to websites that per- mised machines under the control of a single (malicious) form drive-by download attacks [24]. entity. Often, these botnets are rented out to particular Recent studies indicate that, nowadays, about 85% of groups to carry out spam campaigns, in which similar the overall spam traffic on the Internet is sent with the mail messages are sent to a large group of Internet users help of spamming botnets [20,36]. Botnets are networks in a short amount of time. Tracking the bot-infected hosts of compromised machines under the direction of a sin- that participate in spam campaigns, and attributing these gle entity, the so-called botmaster. While different bot- hosts to spam botnets that are active on the Internet, are nets serve different, nefarious goals, one important pur- challenging but important tasks. In particular, this infor- pose of botnets is the distribution of spam emails.
    [Show full text]
  • The History of Spam Timeline of Events and Notable Occurrences in the Advance of Spam
    The History of Spam Timeline of events and notable occurrences in the advance of spam July 2014 The History of Spam The growth of unsolicited e-mail imposes increasing costs on networks and causes considerable aggravation on the part of e-mail recipients. The history of spam is one that is closely tied to the history and evolution of the Internet itself. 1971 RFC 733: Mail Specifications 1978 First email spam was sent out to users of ARPANET – it was an ad for a presentation by Digital Equipment Corporation (DEC) 1984 Domain Name System (DNS) introduced 1986 Eric Thomas develops first commercial mailing list program called LISTSERV 1988 First know email Chain letter sent 1988 “Spamming” starts as prank by participants in multi-user dungeon games by MUDers (Multi User Dungeon) to fill rivals accounts with unwanted electronic junk mail. 1990 ARPANET terminates 1993 First use of the term spam was for a post from USENET by Richard Depew to news.admin.policy, which was the result of a bug in a software program that caused 200 messages to go out to the news group. The term “spam” itself was thought to have come from the spam skit by Monty Python's Flying Circus. In the sketch, a restaurant serves all its food with lots of spam, and the waitress repeats the word several times in describing how much spam is in the items. When she does this, a group of Vikings in the corner start a song: "Spam, spam, spam, spam, spam, spam, spam, spam, lovely spam! Wonderful spam!" Until told to shut up.
    [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]
  • Zerohack Zer0pwn Youranonnews Yevgeniy Anikin Yes Men
    Zerohack Zer0Pwn YourAnonNews Yevgeniy Anikin Yes Men YamaTough Xtreme x-Leader xenu xen0nymous www.oem.com.mx www.nytimes.com/pages/world/asia/index.html www.informador.com.mx www.futuregov.asia www.cronica.com.mx www.asiapacificsecuritymagazine.com Worm Wolfy Withdrawal* WillyFoReal Wikileaks IRC 88.80.16.13/9999 IRC Channel WikiLeaks WiiSpellWhy whitekidney Wells Fargo weed WallRoad w0rmware Vulnerability Vladislav Khorokhorin Visa Inc. Virus Virgin Islands "Viewpointe Archive Services, LLC" Versability Verizon Venezuela Vegas Vatican City USB US Trust US Bankcorp Uruguay Uran0n unusedcrayon United Kingdom UnicormCr3w unfittoprint unelected.org UndisclosedAnon Ukraine UGNazi ua_musti_1905 U.S. Bankcorp TYLER Turkey trosec113 Trojan Horse Trojan Trivette TriCk Tribalzer0 Transnistria transaction Traitor traffic court Tradecraft Trade Secrets "Total System Services, Inc." Topiary Top Secret Tom Stracener TibitXimer Thumb Drive Thomson Reuters TheWikiBoat thepeoplescause the_infecti0n The Unknowns The UnderTaker The Syrian electronic army The Jokerhack Thailand ThaCosmo th3j35t3r testeux1 TEST Telecomix TehWongZ Teddy Bigglesworth TeaMp0isoN TeamHav0k Team Ghost Shell Team Digi7al tdl4 taxes TARP tango down Tampa Tammy Shapiro Taiwan Tabu T0x1c t0wN T.A.R.P. Syrian Electronic Army syndiv Symantec Corporation Switzerland Swingers Club SWIFT Sweden Swan SwaggSec Swagg Security "SunGard Data Systems, Inc." Stuxnet Stringer Streamroller Stole* Sterlok SteelAnne st0rm SQLi Spyware Spying Spydevilz Spy Camera Sposed Spook Spoofing Splendide
    [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]