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Stopping e-mail abuse

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E-mail has become the subject of much abuse, in the form of both spamming and E-mail worm programs. Both of these flood the in-boxes of E-mail users with junk E-mails, wasting their time and money, and often carrying offensive, fraudulent, or damaging content. This article describes the efforts being made to stop E-mail abuse and ensure that E-mail continues to be usable in the face of these threats.

Protection against spam

End users can protect themselves from the brunt of spam's impact in numerous ways.

Spam filters

The continuing increase in spam has resulted in rapid growth in the use of spam filter programs: software designed to examine incoming email and separate spam emails from genuine email messages intended for the user.

Unwanted e-mail can be filtered at the desktop, the network email server/email gateway, the Internet Service Provider's email gateway, or all three locations. While network managers and ISPs can choose hardened email security appliances, services or software designed to interdict both spam and viruses, desktop users are frequently limited to a software-based solution.

A number of commercial spam filtering programs exist and are readily available, but many freeware and shareware spam filters are also available for easy downloading and installation. Spam filters are currently included as standard features in nearly every available email client, though the quality of these built-in filters can be low; for some users, this may necessitate the use of a higher quality filtering solution.

Preventing Address Harvesting

Preventing spammers from obtaining your email address doesn't really solve the spam problem, any more than avoiding all but lowest crime areas of a city solves crime. Many people cannot hide their email addresses and most people want to meet new people via email. They just don't want the flood of spam. It may, however, reduce the amount of spam that you receive.

One way that spammers obtain email addresses to target is to trawl the Web and Usenet for strings which look like addresses, using a spambot. Contact forms and address munging are good ways to prevent email addresses from appearing on these forums. If the spammers can't find the address, the address won't get spam.

There are other ways that spammers can get addresses such as "dictionary attacks" in which the spammer generates a number of likely-to-exist addresses out of names and common words. For instance, if there is someone with the address adam@example.com, where 'example.com' is a popular ISP or mail provider, it is likely that he frequently receives spam.

Address munging

Main article: Address munging

Posting anonymously, or with an entirely faked name and address, is one way to avoid this "address harvesting", but users should ensure that the faked address is not valid. Users who want to receive legitimate email regarding their posts or Web sites can alter their addresses in some way that humans can figure out but spammers haven't (yet). For instance, joe@example.net might post as joeNOS@PAM.example.net, or display his email address as an image instead of text. This is called address munging, from the jargon word "mung" meaning to break.

Contact Forms

Contact forms allow users to send email by filling out forms in a web browser. The web server takes the form data and forwards it to an email address. The user (and therefore the spam harvester) never sees the email address. Contact forms have the drawback that they require a website that supports server side scripts. They are also inconvenient to the message sender as he is not able to use his preferred e-mail client. Finally if the software used to run the contact forms is buggy or badly designed they can become spam tools in their own right.

Disposable e-mail addresses

Many email users sometimes need to give an address to a site without complete assurance that the site will not spam, or leak the address to spammers. One way to mitigate the risk of spam from such sites is to provide a disposable email address -- a temporary address which forwards email to your real account, but which you can disable or abandon whenever you see fit.

A number of services provide disposable address forwarding. Addresses can be manually disabled, can expire after a given time interval, or can expire after a certain number of messages have been forwarded. Some of these services allow easier creation of disposable addresses via various techniques.

Defeating Web bugs and JavaScript

Many modern mail programs incorporate Web browser functionality, such as the display of HTML, URLs, and images. This can easily expose the user to pornographic or otherwise offensive images in spam. In addition, spam written in HTML can contain JavaScript programs to direct the user's Web browser to an advertised page, or to make the spam message difficult or impossible to close or delete. In some cases, spam messages have contained attacks upon security vulnerabilities in the HTML renderer, using these holes to install spyware. (Some computer viruses are borne by the same mechanisms.) Also, the HTML can be used to signal whether a spam message is actually read and seen by a user.

Users can defend against these methods by using mail clients which do not automatically display HTML, images or attachments, or by configuring their clients not to display these by default.

Avoiding responding to spam

It is well established that some spammers regard responses to their messages -- even responses which say "Don't spam me" -- as confirmation that an email address refers validly to a reader. Likewise, many spam messages contain Web links or addresses which the user is directed to follow to be removed from the spammer's mailing list.

In several cases, spam-fighters have tested these links and addresses and confirmed that they do not lead to the recipient address's removal -- if anything, they lead to more spam.

In late 2003, the USA FTC launched a public relations campaign to encourage email users to simply never respond to a spam email -- ever. This campaign stemmed from the tendency of casual email users to reply to spam, in order to complain and request the spammer to cease sending spam.

Perhaps more significantly, since the sender address fields borne by spam messages are almost always forged, a reply to a spam message is likely to reach an innocent third party if it reaches anyone at all.

In Usenet, it is widely considered even more important to avoid responding to spam. Many ISPs have software that seeks out and destroys duplicate messages. Someone may see a spam and respond to it before it is cancelled by their server, which can have the effect of reposting the spammer's spam for them; since it is not just a duplicate, this reposted copy will last longer.

See also the Boulder Pledge.

Reporting spam

The majority of ISPs explicitly forbid their users from spamming, and eject from their service users who are found to have spammed. Tracking down a spammer's ISP and reporting the offense often leads to the spammer's service being terminated. Unfortunately, it can be difficult to track down the spammer -- and while there are some online tools to assist, they are not always accurate. Also occasionally spammers own their own netblocks. In this case the abuse contact for the netblock can be the spammer itself and can confirm your address as live.

Examples of these online tools are SpamCop, Network Abuse Clearinghouse and Blue Frog. These provide automated or semi-automated means to report spam to ISPs. Some spam-fighters regard them as inaccurate compared to what an expert in the email system can do; however, most email users are not experts.

Consumers may also forward "unwanted or deceptive spam" to an email address (spam@uce.gov ) maintained by the FTC. The database so collected is used to prosecute perpetrators of various types of scam or deceptive advertising.

Defense against email worms

In the past several years, scores of worm programs have used email systems as a conduit for infection. The worm program transmits itself in an email message, usually as a MIME attachment. In order to infect a computer, the executable worm attachment must be opened. In almost all cases, this means the user must click on the attachment. The worm also requires a software environment compatible with its programming.

Email users can defend against worms in a number of ways, including:

• Avoiding email client software which supports executable attachments. The most frequently-targeted client software for email worms is Microsoft Outlook and Outlook Express, both of which can easily be made to open executable attachments. However, other Windows-based email software is not immune to worms.
• Using an operating system which does not provide an environment compatible with present worms. Essentially all current email worms affect only the Microsoft Windows operating system. They cannot execute on Macintosh, Unix, GNU/Linux, or other operating systems. In some cases, it is conceivable that a worm could be written for one of these systems; however, various security features militate against it.
• Using up-to-date anti-virus software to detect incoming worms and quarantine or delete them before they can take effect.
• Being skeptical of unsolicited email attachments. Since worms and other email-borne malware arrive in this form, some email users simply refuse to open attachments that the sender has not given them advance notice of.

Examination of anti-spam methods

There are a number of services and software systems that mail sites and users can use to reduce the load of spam on their systems and mailboxes. Some of these depend upon rejecting email from Internet sites known or likely to send spam. Others rely on automatically analyzing the content of email messages and weeding out those which resemble spam. These two approaches are sometimes termed blocking and filtering.

Blocking and filtering each have their advocates and advantages. While both reduce the amount of spam delivered to users' mailboxes, blocking does much more to alleviate the bandwidth cost of spam, since spam can be rejected before the message is transmitted to the recipient's mail server. Filtering tends to be more thorough, since it can examine all the details of a message. Many modern spam filtering systems take advantage of machine learning techniques, which vastly improve their accuracy over manual methods. However, some people find filtering intrusive to privacy, and many mail administrators prefer blocking to deny access to their systems from sites tolerant of spammers.

DNSBLs

Main article: DNSBL

DNS-based Blackhole Lists, or DNSBLs, are used for heuristic filtering and blocking. A site publishes lists (typically of IP addresses) via the DNS, in such a way that mail servers can easily be set to reject mail from those sources. There are literally scores of DNSBLs, each of which reflects different policies: some list sites known to emit spam; others list open mail relays or proxies; others list ISPs known to support spam. Other DNS-based anti-spam systems list known good ("white") or bad ("black") IPs domains or URLs, including RHSBLs and URIBLs. For history, details, and examples of DNSBLs, see DNSBL.

Content-based filtering

Until recently, content filtering techniques relied on mail administrators specifying lists of words or regular expressions disallowed in mail messages. Thus, if a site receives spam advertising "herbal Viagra", the administrator might place these words in the filter configuration. The mail server would thence reject any message containing the phrase.

Content based filtering can also filter based on content other than the words and phrases that make up the body of the message. Primarily, this means looking at the header of the email, the part of the message that contains information about the message, and not the body text of the message. Spammers will often spoof fields in the header in order to hide their identities, or to try to make the email look more legitimate than it is; many of these spoofing methods can be detected. Also, spam sending software often produces a header that violates the RFC 2822 standard on how the email header is supposed to be formed.

Disadvantages of this static filtering are threefold: First, it is time-consuming to maintain. Second, it is prone to false positives. Third, these false positives are not equally distributed: manual content filtering is prone to reject legitimate messages on topics related to products advertised in spam. A system administrator who attempts to reject spam messages which advertise mortgage refinancing may easily inadvertently block legitimate mail on the same subject.

Finally, spammers can change the phrases and spellings they use, or employ methods to try to trip up phrase detectors. This means more work for the administrator. However, it also has some advantages for the spam fighter. If the spammer starts spelling "Viagra" as "V1agra" or "Via_gra", it makes it harder for the spammer's intended audience to read their messages. If they try to trip up the phrase detector, by, for example, inserting an invisible-to-the-user HTML comment in the middle of a word ("Via<!---->gra"), this sleight of hand is itself easily detectable, and is a good indication that the message is spam. And if they send spam that consists entirely of images, so that anti-spam software can't analyze the words and phrases in the message, the fact that there is no readable text in the body can be detected.

However, content filtering can also be implemented by examining the URLs present (i.e. spamvertised) in an email message. This form of content filtering is much harder to disguise as the URLs must resolve to a valid domain name. Extracting a list of such links and comparing them to published sources of spamvertised domains is a simple and reliable way to eliminate a large percentage of spam via content analysis.

Statistical filtering

Statistical filtering was first proposed in 1998 by Mehran Sahami et al., at the AAAI-98 Workshop on Learning for Text Categorization. A statistical filter is a kind of document classification system, and a number of machine learning researchers have turned their attention to the problem. Statistical filtering was popularized by Paul Graham's influential 2002 article A Plan for Spam, which proposed the use of naive Bayes classifiers to predict whether messages are spam or not