Email Spam

E-mail spam, also known as junk e-mail, is a subset of spam that involves nearly identical messages sent to numerous recipients by e-mail. A common synonym for spam is unsolicited bulk e-mail (UBE). Definitions of spam usually include the aspects that email is unsolicited and sent in bulk. "UCE" refers specifically to unsolicited commercial e-mail.

E-mail spam has steadily, even exponentially grown since the early 1990s to several billion messages a day. Spam has frustrated, confused, and annoyed e-mail users. The total volume of spam (over 100 billion emails per day as of April 2008) has leveled off slightly in recent years, and is no longer growing exponentially. The amount received by most e-mail users has decreased, mostly because of better filtering. About 80% of all spam is sent by fewer than 200 spammers. Botnets, networks of virus-infected computers, are used to send about 80% of spam. Since the cost of the spam is borne mostly by the recipient, it is effectively postage due advertising.

The legal status of spam varies from one jurisdiction to another. In the United States, spam was declared to be legal by the CAN-SPAM Act of 2003 provided the message adheres to certain specifications. ISPs have attempted to recover the cost of spam through lawsuits against spammers, although they have been mostly unsuccessful in collecting damages despite winning in court.

Spammers collect e-mail addresses from chatrooms, websites, customer lists, newsgroups, and viruses which harvest users' address books, and are sold to other spammers. Much of spam is sent to invalid e-mail addresses. Spam averages 94% of all e-mail sent.

Address Munging

Posting anonymously, or with a fake name and address, is one way to avoid e-mail address harvesting, but users should ensure that the fake address is not valid. Users who want to receive legitimate email regarding their posts or Web sites can alter their addresses so humans can figure out but spammers cannot. For instance, joe@example.net might post as joeNOS@PAM.example.net.invalid. Address munging, however, can cause legitimate replies to be lost. If it's not the user's valid address, it has to be truly invalid, otherwise someone or some server will still get the spam for it.[1] Other ways use transparent address munging to avoid this by allowing users to see the actual address but obfuscate it from automated email harvesters with methods such as displaying all or part of the e-mail address on a web page as an image, a text logo shrunken to normal size using in-line CSS, or as jumbled text with the order of characters restored using CSS.

Email Filtering

Email filtering is the processing of e-mail to organize it according to specified criteria. Most often this refers to the automatic processing of incoming messages, but the term also applies to the intervention of human intelligence in addition to anti-spam techniques, and to outgoing emails as well as those being received.
Email filtering software inputs email. For its output, it might pass the message through unchanged for delivery to the user's mailbox, redirect the message for delivery elsewhere, or even throw the message away. Some mail filters are able to edit messages during processing.

Antispam Techniques

To prevent e-mail spam, both end users and administrators of e-mail systems use various anti-spam techniques. Some of these techniques have been embedded in products, services and software to ease the burden on users and administrators. No one technique is a complete solution to the spam problem, and each has trade-offs between incorrectly rejecting legitimate e-mail vs. not rejecting all spam, and the associated costs in time and effort.
Anti-spam techniques can be broken into four broad categories: those that require actions by individuals, those that can be automated by e-mail administrators, those that can be automated by e-mail senders and those employed by researchers and law enforcement officials.

Detecting Spam

People tend to be much less bothered by spam slipping through filters into their mail box (false negatives), than having desired e-mail ("ham") blocked (false positives). Trying to balance false negatives (missed spams) vs false positives (rejecting good e-mail) is critical for a successful anti-spam system. Some systems let individual users have some control over this balance by setting "spam score" limits, etc. Most techniques have both kinds of errors, to varying degrees. So, for example, anti-spam systems may use techniques that have a high false negative rate (miss a lot of spam), in order to reduce the number of false positives (rejecting good e-mail),
Detecting spam based on the content of the e-mail, either by detecting keywords such as "viagra" or by statistical means, is very popular. Such methods can be very accurate when they are correctly tuned to the types of legitimate email that an individual gets, but they can also make mistakes such as detecting the keyword "cialis" in the word "specialist"; see also Internet censorship#"By-catch". The content also doesn't determine whether the email was either unsolicited or bulk, the two key features of spam. So, if a friend sends you a joke that mentions "viagra", content filters can easily mark it as being spam even though it is neither unsolicited nor sent in bulk.
The most popular DNSBLs (DNS Blacklists) are lists of IP addresses of known spammers, open relays, zombie spammers etc.
Spamtraps are often email addresses that were never valid or have been invalid for a long time that are used to collect spam. An effective spamtrap is not announced and is only found by dictionary attacks or by pulling addresses off hidden webpages. For a spamtrap to remain effective the address must never be given to anyone. Some black lists, such as spamcop, use spamtraps to catch spammers and blacklist them.
Enforcing technical requirements of the Simple Mail Transfer Protocol (SMTP) can be used to block mail coming from systems that are not compliant with the RFC standards. A lot of spammers use poorly written software or are unable to comply with the standards because they do not have legitimate control of the computer sending spam (zombie computer). So by setting restrictions on the mail transfer agent (MTA) a mail administrator can reduce spam significantly, such as by enforcing the correct fall back of Mail eXchange (MX) records in the Domain Name System, or the correct handling of delays (Teergrube).

Introduction

Spam and fraudulent e-mail messages are major issues for computer users and businesses of all sizes. Companies are being forced to commit significant resources to protect their messaging infrastructure and their brand from these abuses, and computer users must work to protect themselves from the influx of deceptive e-mail. Network infrastructure is being taxed. Spam was once just an annoyance, but it has now become the tactic of choice for online deception, fraud, and abuse.
To help detect and prevent this onslaught of abuse and deception, Microsoft has developed a holistic strategy that includes industry collaboration, prescriptive education, and the development of innovative technologies and antispam services. These technologies are found in all Microsoft e-mail clients, servers, and services, including Windows Live Hotmail, Microsoft Office Outlook, Microsoft Exchange Server, and Microsoft Exchange Hosted Filtering.

Microsoft Exchange Hosted Filtering

Microsoft Exchange Hosted Filtering, formally FrontBridge Services, is an e-mail filtering service that stops spam and viruses before they reach the corporate network. A global, load-balanced network of data centers ensures reliable e-mail delivery even during the largest spam and virus attacks. A sophisticated, layered e-mail filtering approach improves employee productivity through high spam-capture rates with almost no false positives. Using a managed service model, Exchange Hosted Filtering reduces complexity in the IT environment, frees IT resources for more strategic corporate initiatives, allows for a predictable service payment regardless of increases in spam and virus activity, and eliminates the costs required to scale to increasingly severe messaging threats. Network performance and filtering accuracy are backed by a comprehensive set of service level agreements (SLAs).

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