If people who receive their unsolicited email, complain to your Internet service provider (ISP), or the company that hosts your website (your web server), you may end up losing both. This means your website will be closed and you lose all Internet connectivity.

Of course you can get another Internet access provider, but this may hurt you in three ways:
+ It will be a waste of time
+ It will cost money
+ Damage your reputation

Therefore this method of free promotion online may be the source of many problems, and the best thing you can do is not engage with him.

Paul Judge

CDT embarked on a project to attempt to determine the source of spam. To do so, we set up hundreds of different e-mail addresses, used them for a single purpose, and then waited six months to see what kind of mail those addresses were receiving. It should come as no surprise to most e-mail users that many of the addresses CDT created for this study attracted spam, but it is very interesting to see the different ways that e-mail addresses attracted spam ? and the different volumes ? depending on where the e-mail addresses were used.

The results offer Internet users insights about what online behavior results in the most spam. The results also debunk some of the myths about spam.

Major Findings

? Our analysis indicated that e-mail addresses posted on Web sites or in newsgroups attract the most spam.
o Web Sites ? CDT received the most e-mails when an address was placed visibly on a public Web site. Spammers use software harvesting programs such as robots or spiders to record e-mail addresses listed on Web sites, including both personal Web pages and institutional (corporate or non-profit) Web pages.
CDT tested two methods of obstructing address harvesting:

Replacing characters in an e-mail address with human-readable equivalents, e.g. “example@domain.com” was written “example at domain dot com;” and

Replacing characters in an e-mail address with HTML equivalents.
E-mail addresses posted to Web sites using these conventions did not receive any spam.

o USENET newsgroups ? Newsgroups can expose to spammers the e-mail address of every person who posts to the newsgroup. Newsgroup postings, on average, generated less spam than posting an e-mail address on a high-traffic web site. In our study, we discovered that most newsgroup-related spam is sent to the address in the message header, even if other e-mail addresses are included in the text of the posting.

? For the most part, companies that offered users a choice about receiving commercial e-mails respected that choice. Most of the major Web sites to which we provided e-mail addresses respected the privacy choices we made ? when a choice was made available to us.

? Some spam is generated through attacks on mail servers, methods that don’t rely on the collection of e-mail addresses at all. In “brute force” attacks and “dictionary” attacks, spam programs send spam to every possible combination of letters at a domain, or to common names and words. While these attacks can be blocked, some spam is likely to get through. In many cases, spam generated by these attacks will be directed to shorter e-mail address (like bob@domain.com) before it is directed to longer addresses (like bobwilliams@domain.com).

Tips for Avoiding Spam

Currently there is no foolproof way to prevent spam. Based on our research, we recommend that Internet users try the following methods to prevent spam:

? Disguise e-mail addresses posted in a public electronic place.
CDT received the most spam just by placing an e-mail address at the bottom of a webpage. Spammers “harvest” these addresses with computer programs that collect and process addresses and add them to spam mailing lists. If a user must post his/her e-mail address in a public place, it is useful to disguise the address through simple means such as replacing “example@domain.com” with “example at domain dot com” or other variations such as the HTML numeric equivalent, in which “example@domain.com” could be written “example@d omain.com.”
Opt out of member directories that may place your e-mail address online. If your employer places your e-mail address online, ask the Webmaster to make sure it is disguised in some way.

? Read carefully when filling out online forms requesting your e-mail address, and exercise your choice.
If you don’t want to receive e-mail from a Web site operator, don’t give them your e-mail address unless they offer the option of declining to receive e-mail and you exercise that option. If you are asked for your e-mail address in an online setting such as a form, make sure you pay attention to any options discussing how the address will be used. Pay attention to check boxes that request the right to send you e-mails or share your e-mail address with partners. Read the privacy policies of Web sites. If you suspect that a Web site has violated its privacy policy, you can report it to your state attorney general or the Federal Trade Commission.

? Use multiple e-mail addresses.
When using an unfamiliar Web site or posting to a newsgroup, establish an e-mail address for that specific purpose. Alternatively, instead of just using one or two e-mail addresses, you can use “disposable e-mail addresses,” which consolidate e-mail in a single location but allow you to immediately shut off any address that is attracting spam. By recording which disposable address was used at which web site, one can track what sites are causing spam. Many Web sites are now providing free e-mail accounts. A search in Google Directory for “disposable e-mail addresses” provides a list of e-mail providers designed for one-time use e-mails.

? Use a filter.
Many ISPs and free e-mail services now provide spam filtering. While filters are not perfect, they can cut down tremendously the amount of spam a user receives.

? Short e-mail addresses are easy to guess, and may receive more spam.
At least one spammer tried to guess the e-mail addresses used in this study by sending mail to short and common addresses. E-mail addresses composed of short names and initials like bob@ or tse@, or basic combinations like smithj@ or toms@ will probably receive more spam. E-mail addresses need not be incomprehensible, but a user with a common or short name may want to modify or add to it in some way in his or her e-mail address.
Conclusions
1. E-mail addresses harvested from the public Web are frequently used by spammers. By an overwhelming margin, the greatest amount of spam we received was to addresses posted on the public Web.
When an address has been posted on the public Web, it can potentially be viewed by hundreds of millions of users. People who develop spam lists exploit this feature by using address-harvesting programs to surf across thousands of web sites, collecting any e-mail addresses that they encounter. Most users have no idea that their addresses have been harvested until they begin receiving spam.
2. The amount of spam received by an address posted on the public Web is directly related to the amount of traffic that Web site receives. The more visitors a Web site has in a given period of time, the greater the likelihood that an address-harvesting program used to send spam will scour it. As a result, addresses posted on high-traffic Web sites are likely to receive a greater amount of spam than address posted on smaller sites ? popular Web sites are more frequently “harvested,” and addresses posted on those Web sites are added to a greater number of spam lists.

3. E-mail addresses harvested from the public Web appear to have a relatively short “shelf life.” When e-mail addresses we posted on the public Web were removed, there was a pronounced drop in the amount of spam they received each day. The change was not absolute ? on a given day, an address might receive a few spam messages even months after it had been removed from the public Web. But such spam was on the order of 2 or 3 messages per day, compared to the thirty or more messages received by addresses still on the public Web.

4. Addresses posted in the headers of USENET messages can receive significant spam, though less than a posting on the public Web. Like most Web sites, USENET postings are publicly accessible and may be targeted by e-mail address-harvesting programs. When a user includes his or her address in the heading of a USENET message, that address can be harvested and used to send spam. Our preliminary data indicates that some USENET newsgroups are more frequently harvested for e-mail addresses than others.

5. Obscuring an e-mail address is an effective way to avoid spam from harvesters on the Web or on USENET newsgroups. Even when posted in publicly accessible areas, none of the addresses we obscured ? whether in English (”example at domain dot com”) or in HTML ? received a single piece of spam. Users who want to avoid spam should consider obscuring their addresses when possible.

6. Sites that publish their policies and make choice available to users generally respected those policies. A major element of the CDT project was to submit e-mail addresses to a number of popular businesses and other organizations on the Web. Many of these sites had privacy policies describing how they handle e-mail addresses and other potentially sensitive pieces of information. While the terms of these policies varied, we found that almost all sites followed their policies. In addition, when consumers were offered choices about how their personal information would be handled, those choices were respected.

7. Domain name registration does not seem to be a major source of spam. Despite the fact that the WHOIS database is publicly accessible, our project received just a single spam message to an address that was in WHOIS for six months. This leads us to believe that, at least for some people registering new domain names, listings in the WHOIS database may not be a major source of spam. However, because our project had a relatively short duration, we were not able to examine whether additional spam would be received as a domain name approached its renewal date.

8. Even when an e-mail address has not been posted or shared in any way, it is still possible to receive spam through various “attacks” on a mail server. In our study, a “brute force” attack on the mail server generated a tremendous amount of spam, even to addresses that hadn’t been shared anywhere. Anecdotal evidence from network operators indicates that such attacks are not uncommon, and that while alert network administrators can sometimes block them, a significant amount of spam can still result. Sometimes, these attacks take the form of “dictionary attacks,” in which the attacker sends e-mail to all the words in the dictionary, or attacks in which e-mail is sent to common surnames and first initials (such as “jsmith” or “bjones”). For individual Internet users, there is little that can be done to avoid the spam that may result from such attacks.

Spam is an ineffective way to advertise as it provides people with information about products and services that likely are of use to no one. Many spammers are unaware of what they are doing and have probably been duped into some kind of “Get-Rich-Quick” scheme. In a article about the commercial uses of spam (from Alchemy Mindworks) it was explained the reason that people spam and how they might be unaware of the damage they are causing:

The most prevalent sort of junk e-mail is commercial advertising. Judging by the content of most of these messages, their perpetrators have all just signed up with an Internet access provider, and were given complimentary copies of one of the many “How to Make Lots of Money on the Internet” books. Some of them are genuinely inconsiderate of the rights of other users of the net ? the bulk of them, however, are merely confused, deluded and ignorant.

These unsolicited emails take time out of someone busy day to either read or get rid off; they become a constant hassle to your everyday routine. Another way that people get on the spam lists in the first place is when spammers get a hold of mailing list for an organization or forum:

One particularly nasty variant of email spam is sending spam to mailing lists (public or private email discussion forums.) Because many mailing lists limit activity to their subscribers, spammers will use automated tools to subscribe to as many mailing lists as possible, so that they can grab the lists of addresses, or use the mailing list as a direct target for their attacks.

Spam can come from anywhere and internet users should be very careful about who they give their email address too.

Ways to get rid of spam are ever rising and slowing down the amount of junk mail that email users receive. For those who use programs to read their emails there are solutions such as SpamCop a highly rated program and the slightly over zealous Spam Hater, both programs not only stop spam but report complaints to the senders of the spam. Online email such as Hotmail also have taken measure to prevent spam in a help file one can learn,
In MSN Hotmail, you have several ways to protect yourself from junk mail: On the page, set up the Junk Mail Filter to redirect spam to your Junk Mail folder.
You can set the Junk Mail Filter to High or Exclusive, and then create a Safe List of addresses that should always send messages to your Inbox… If an offending message still gets into your Inbox, click the check box to the left of the message, and then click Block to stop future messages from that sender from entering your Inbox.

Other measures should be taken as checking out a website or forum for a privacy policy before giving away your email address and when signing up for a service be sure not to check anything that says that you would like to receive “special offers” or “important information” these can be a red flag that the service is linked to potential spammers.

Spam is a nuisance and will hopefully be eliminated or at least slowed down over time. More people learn how to stop or prevent it everyday and hopefully one day it will no longer be in existence. Until then, everyone must take measures to protect their inbox from being crammed full of emails about something no one could ever want.

We’ve all seen that comment spam is becoming a serious problem. Particularly on Movable Type weblogs, where the generated pages are all very similar in structure and semantics, spammers are abusing comment systems to increase their rank on Google.

Even more frustrating than the spamming problem is the fact that there isn’t a simple solution that will work for everyone and that all options have their own sets of pros and cons. During the past couple of months, we’ve been throwing around ideas at Six Apart about the best ways to combat spammers.

Readers of your weblog must register before posting to your weblog.
Before someone can post a comment to your weblog, they must register with your site.

For many webloggers, this solution is not ideal. Informal polling of webloggers has revealed that many do not want to require someone to register before posting. It usually discourages conversations from forming and is a barrier for open discussion. Additionally, without federation, logins on multiple weblogs become unmanageable.

While we do plan on integrating comment registration into Movable Type Pro (which we’ll be talking about in more detail very soon), it’s an option that serves a different purpose than just blocking spam. If you want to prevent links to explicit pornography from appearing on your site, you shouldn’t have to be required to turn on comment registration.

Comments require approval before being posted When a comment is posted, you can receive an email that provides a clickable link you must visit before the comment can be posted on your site.

For webloggers with a small amount of readers, this solution may be ideal. However, if you receive a good deal of comments, it’s a solution that doesn’t scale. Additionally, it may ruin the spontaneity of discussion.

Image comprehension technology
Before a comment can be posted on a weblog, human eyes must enter a code that, ideally, is not readable by a computer.

This solution is not feasible because of accessibility issues. Additionally, spammers seem to be searching with bots and entering spam manually.

A possible solution for everyone?
The problem has intensified in the past couple of weeks, but the good news is that as more people have been hit by comment spam, actual solutions are beginning to emerge.

Specifically, Jay Allen’s MT-BlackList is a blacklist-based solution to comment spam for Movable Type weblogs. It checks the comment fields (body, URL, author, etc) for URLs commonly found in spam comments, and rejects the comment if it looks like spam. The core plugin is set to be released today (Monday), but one of its neatest features-in-development is the ability for weblog systems to share blacklist data using XML-RPC. This provides the basis of a collaborative system similar to Razor, with the option for more management over the items in your own system’s blacklist.

We’re deeply committed to finding a way to combat spammers and we’re determined to do it on a core system level so that everyone can take advantage of spam prevention. We’re working on integrating comment spam blocking for MT and TypePad, and the great thing about Jay’s solution is that it could be the start of a distributed spam blocking network for comments, an implementation of which could be included in multiple tools. But, like email, there isn’t one simple solution that can be switched on and end spam completely. Hopefully we’re moving a step closer.

The goal of this project is to provide a large repository of spam that can be used by researchers and tool developers. In the past, there were a few small personal spam archives that were used. There was no large set of spam that could be used to test new anti-spam algorithms.

Thus, developers could not sufficiently test their techniques across a range of messages. Also, the lack of a “standard” sample of spam made it difficult to effectively benchmark anti-spam tools.

The problem of unsolicited e-mail has been increasing for years, but help has arrived. In this article, David discusses and compares several broad approaches to the automatic elimination of unwanted e-mail while introducing and testing some popular tools that follow these approaches.

Unethical e-mail senders bear little or no cost for mass distribution of messages, yet normal e-mail users are forced to spend time and effort purging fraudulent and otherwise unwanted mail from their mailboxes. In this article, I describe ways that computer code can help eliminate unsolicited commercial e-mail, viruses,
trojans, and worms, as well as frauds perpetrated electronically and other undesired and troublesome e-mail. In some sense, the final and best solution for eliminating spam will probably take place on a legal level. In the meantime, however, you can do some things from a code perspective that can serve as an interim solution to the problem, until (if ever) the laws begin to evolve at the same rate as public frustration.

Considering matters technically ? but also with common sense ? what is generally called “spam” is somewhat broader than the category “unsolicited commercial e-mail”; spam encompasses all the e-mail that we do not want and that is only very loosely directed at us. Such messages are not always commercial per se, and some push the limits of what it means to be solicited. For example, we do not want to get viruses (even from our unwary friends); nor do we generally want chain letters, even if they don’t ask for money; nor proselytizing messages from strangers; nor outright attempts to defraud us. In any case, it is usually
unambiguous whether a message is spam, and many, many people get the same such
e-mails.

The problem of unsolicited e-mail has been increasing for years, but help has arrived. In this article, David discusses and compares several broad approaches to the automatic elimination of unwanted e-mail while introducing and testing some popular tools that follow these approaches.Unethical e-mail senders bear little or no cost for mass distribution of messages, yet normal e-mail users are forced to spend time and effort purging fraudulent and otherwise unwanted mail from their mailboxes. In this article, I describe
ways that computer code can help eliminate unsolicited commercial e-mail, viruses, trojans, and worms, as well as frauds perpetrated electronically and other undesired and troublesome e-mail. In some sense, the final and best solution for eliminating spam will probably take place on a legal level. In the meantime, however, you
can do some things from a code perspective that can serve as an interim solution to the problem, until (if ever) the laws begin to evolve at the same rate as public frustration.

Considering matters technically ? but also with common sense ? what is generally called “spam” is somewhat broader than the category “unsolicited commercial e-mail”; spam encompasses all the e-mail that we do not want and that is only very loosely directed at us. Such messages are not always commercial per se, and some push the limits of what it means to be solicited. For example, we do not want to get viruses (even from our unwary friends); nor do we generally want chain letters, even if they don’t ask for money; nor proselytizing messages from strangers; nor outright attempts to defraud us. In any case, it is usually
unambiguous whether a message is spam, and many, many people get the same such e-mails.

The problem with spam is that it tends to sware spams than I did legitimate correspondences. On average, I probably get 10 spams for every appropriate e-mail. In some ways I am unusual ? as a public writer, I maintain a widely published e-mail address; moreover, I both welcome and receive frequent correspondence
from strangers related to my published writing and to my software libraries. Unfortunately, a letter from a stranger ? with who-knows-which e-mail application, OS, native natural language, and so on, is not immediately obvious in its purpose; and spammers try to slip their messages underneath such ambiguities. My seconds are valuable to me, especially when they are claimed many times during every hour of a day.

Hiding contact information:

For some e-mail users, a reasonable, sufficient, and very simple approach to avoiding spam is simply to guard e-mail addresses closely. For these people, an e-mail address is something to be revealed only to selected, trusted parties. As extra precautions, an e-mail address can be chosen to avoid easily guessed names and dictionary words, and addresses can be disguised when posting to public areas. We have all seen e-mail addresses cutely encoded in forms like “echo zregm@tabfvf.pk | tr A-Za-z N-ZA-Mn-za-m”.

In addition to hiding addresses, a secretive e-mailer often uses one or more of the free e-mail services for “throwaway” addresses. If you need to transact e-mail with a semi-trusted party, a temporary address can be used for a few days, then abandoned along with any spam it might thereafter accumulate. The
real “confidantes only” address is kept protected.

In my informal survey of discussions of spam on Web-boards, mailing lists, the Usenet, and so on, I’ve found that a category of e-mail users gains sufficient protection from these basic precautions.

Looking at filtering software:

This article looks at filtering software from a particular perspective. I want to know how well different approaches work in correctly identifying spam as spam and desirable messages as legitimate. For purposes of answering this question, I am not particularly interested in the details of configuring filter applications
to work with various Mail Transfer Agents (MTAs). There is certainly a great deal of arcana surrounding the best configuration of MTAs such as Sendmail, QMail, Procmail, Fetchmail, and others. Further, many e-mail clients have their own filtering options and plug-in APIs. Fortunately, most of the filters I look at come with pretty good documentation covering how to configure them with various
MTAs.

For purposes of my testing, I developed two collections of messages: spam and legitimate. Both collections were taken from mail I actually received in the last couple of months, but I added a significant subset of messages up to several years old to broaden the test. I cannot know exactly what will be contained
in next month’s e-mails, but the past provides the best clue to what the future holds. That sounds cryptic, but all I mean is that I do not want to limit the patterns to a few words, phrases, regular expressions, etc. that might characterize the very latest e-mails but fail to generalize to the two types.

A general comment on testing is worth emphasizing. False negatives in spam filters just mean that some unwanted messages make it to your inbox. Not a good thing, but not horrible in itself. False positives are cases where legitimate messages are misidentified as spam. This can potentially be very bad, as some legitimate messages are important, even urgent, in nature, and even those that are merely conversational are ones we do not want to lose. Most filtering software allows you to save rejected messages in temporary folders pending review ? but if you need to review a folder full of spam, the usefulness of the software is
thereby reduced.

1. Basic structured text filters

The e-mail client I use has the capability to sort incoming e-mail based on simple strings found in specific header fields, the header in general, and/or in the body. Its capability is very simple and does not even include regular expression matching. Almost all e-mail clients have this much filtering capability.

Over the last few months, I have developed a fairly small number of text filters. These few simple filters correctly catch about 80% of the spam I receive. Unfortunately, they also have a relatively high false positive rate ? enough that I need to manually examine some of the spam folders from time to time. (I sort probable spam into several different folders, and I save them all to develop message corpora.) Although exact details will differ among users, a general pattern will be useful to most readers:

? Set 1: A few people or mailing lists do funny things
with their headers that get them flagged on other rules. I catch something in
the header (usually the From:) and whitelist it (either to INBOX or somewhere
else).

? Set 2: In no particular order, I run the following
spam filters:

o Identify a specific bad sender.

o Look for “<>” as the From: header.

o Look for “@<” in the header. The training sets are about twice as large.

A general comment on testing is worth emphasizing. False negatives in spam filters just mean that some unwanted messages make it to your inbox. Not a good thing, but not horrible in itself. False positives are cases where legitimate messages are misidentified as spam. This can potentially be very bad, as some legitimate messages are important, even urgent, in nature, and even those that are merely conversational are ones we do not want to lose. Most filtering software allows you to save rejected messages in temporary folders pending review — but if you need to review a folder full of spam, the usefulness of the software is
thereby reduced.

1. Basic structured text filters:

The e-mail client I use has the capability to sort incoming e-mail based on
simple strings found in specific header fields, the header in general, and/or
in the body. Its capability is very simple and does not even include regular
expression matching. Almost all e-mail clients have this much filtering capability.

Over the last few months, I have developedhis for some reason).

o Look for “Content-Type: audio”. Nothing I want has this, only virii (your
mileage may vary).

o Look for “euc-kr” and “ks_c_5601-1987? in the headers. I can’t read that language,
but for some reason I get a huge volume of Korean spam (of course, for an actual
Korean reader, this isn’t a good rule).

? Set 3: Store messages to known legitimate addresses.
I have several such rules, but they all just match a literal To: field.

? Set 4: Look for messages that have a legit address
in the header, but that weren’t caught by the previous To: filters. I find that
when I am only in the Bcc: field, it’s almost always an unsolicited mailing
to a list of alphabetically sequential addresses (mertz1@…, mertz37@…, etc).

? Set 5: Anything left at this point is probably spam
(it probably has forged headers to avoid identification of the sender).

2. Whitelist/verification filters:

A fairly aggressive technique for spam filtering is what I would call the “whitelist
plus automated verification” approach. There are several tools that implement
a whitelist with verification: TDMA is a popular multi-platform open source
tool; ChoiceMail is a commercial tool for Windows; most others seem more preliminary.
(See Resources later in this article for links.)

A whitelist filter connects to an MTA and passes mail only from explicitly approved
recipients on to the inbox. Other messages generate a special challenge response
to the sender. The whitelist filter’s response contains some kind of unique
code that identifies the original message, such as a hash or sequential ID.
This challenge message contains instructions for the sender to reply in order
to be added to the whitelist (the response message must contain the code generated
by the whitelist filter). When a legitimate sender answers a challenge, her/his
address is added to the whitelist so that any future messages from the same
address are passed through automatically.

Although I have not used any of these tools more than experimentally myself,
I would expect whitelist/verification filters to be very nearly 100% effective
in blocking spam messages. It is conceivable that spammers will start adding
challenge responses to their systems, but this could be countered by making
challenges slightly more sophisticated (for example, by requiring small human
modification to a code). Spammers who respond, moreover, make themselves more
easily traceable for people seeking legal remedies against them.

The problem with whitelist/verification filters is the extra burden they place
on legitimate senders. Inasmuch as some correspondents may fail to respond to
challenges ? for any reason ? this makes for a type of false positive. In
the best case, a slight extra effort is required for legitimate senders. But
senders who have unreliable ISPs, picky firewalls, multiple e-mail addresses,
non-native understanding of English (or whatever language the challenge is written
in), or who simply overlook or cannot be bothered with challenges, may not have
their legitimate messages delivered. Moreover, sometimes legitimate “correspondents”
are not people at all, but automated response systems with no capability of
challenge response. Whitelist/verification filters are likely to require extra
efforts to deal with mailing-list signups, online purchases, Web site registrations,
and other “robot correspondences”.

3. Distributed adaptive blacklists:

Spam is almost by definition delivered to a large number of recipients. And
as a matter of practice, there is little if any customization of spam messages
to individual recipients. Each recipient of a spam, however, in the absence
of prior filtering, must press his own “Delete” button to get rid of the message.

Tools such as Razor and Pyzor (see Resources) operate around servers that store
digests of known spams. When a message is received by an MTA, a distributed
blacklist filter is called to determine whether the message is a known spam.
These tools use clever statistical techniques for creating digests, so that
spams with minor or automated mutations (or just different headers resulting
from transport routes) do not prevent recognition of message identity. In addition,
maintainers of distributed blacklist servers frequently create “honey-pot” addresses
specifically for the purpose of attracting spam (but never for any legitimate
correspondences). In my testing, I found zero false positive spam categorizations
by Pyzor. I would not expect any to occur using other similar tools, such as
Razor.

There is some common sense to this. Even those ill-intentioned enough to taint
legitimate messages would not have samples of my good messages to report to
the servers ? it is generally only the spam messages that are widely distributed.
It is conceivable that a widely sent, but legitimate message such as the developerWorks
newsletter could be misreported, but the maintainers of distributed blacklist
servers would almost certainly detect this and quickly correct such problems.

As the summary table below shows, however, false negatives are far more common
using distributed blacklists than with any of the other techniques I tested.
The authors of Pyzor recommend using the tool in conjunction with other techniques
rather than as a single line of defense. While this seems reasonable, it is
not clear that such combined filtering will actually produce many more spam
identifications than the other techniques by themselves.

In addition, since distributed blacklists require talking to a server to perform
verification, Pyzor performed far more slowly against my test corpora than did
any other techniques.

4. Rule-based rankings:

The most popular tool for rule-based spam filtering, by a good margin, is SpamAssassin.
There are other tools, but they are not as widely used or actively maintained.
SpamAssassin (and similar tools) evaluate a large number of patterns ? mostly
regular expressions ? against a candidate message. Some matched patterns add
to a message score, while others subtract from it. If a message’s score exceeds
a certain threshold, it is filtered as spam; otherwise it is considered legitimate.

Some ranking rules are fairly constant over time ? forged headers and auto-executing
JavaScript, for example, almost timelessly mark spam. Other rules need to be
updated as the products and scams advanced by spammers evolve. Herbal Viagra
and heirs of African dictators might be the rage today, but tomorrow they might
be edged out by some brand new snake-oil drug or pornographic theme. As spam
evolves, SpamAssassin must evolve to keep up with it.

The README for SpamAssassin makes some very strong claims:

In its most recent test, SpamAssassin differentiated between spam and non-spam
mail correctly in 99.94% of cases. Since then, it’s just been getting better
and better!

My testing showed nowhere near this level of success. Against my corpora, SpamAssassin
had about 0.3% false positives and a whopping 19% false negatives. In fairness,
this only evaluated the rule-based filters, not the optional checks against
distributed blacklists. Additionally, my spam corpus is not purely spam ? it
also includes a large collection of what are probably virus attachments (I do
not open them to check for sure, but I know they are not messages I authorized).

SpamAssassin runs much quicker than distributed blacklists, which need to query
network servers. But it also runs much slower than even non-optimized versions
of the below statistical models (written in interpreted Python using naive data
structures).

5. Bayesian word distribution filters:

Paul Graham wrote a provocative essay in August 2002. In “A Plan for Spam” (see
Resources later in this article), Graham suggested building Bayesian probability
models of spam and non-spam words. Graham’s essay, or any general text on statistics
and probability, can provide more mathematical background than I will here.

The general idea is that some words occur more frequently in known spam, and
other words occur more frequently in legitimate messages. Using well-known mathematics,
it is possible to generate a “spam-indicative probability” for each word. Another
simple mathematical formula can be used to determine the overall “spam probability”
of a novel message based on the collection of words it contains.

Graham’s idea has several noteworthy benefits:

1. It can generate a filter automatically from corpora of categorized messages
rather than requiring human effort in rule development.

2. It can be customized to individual users’ characteristic spam and legitimate
messages.

3. It can be implemented in a very small number of lines of code.

4. It works surprisingly well.

At first blush, it would be reasonable to suppose that a set of hand-tuned and
laboriously developed rules like those in SpamAssassin would predict spam more
accurately than a scattershot automated approach. It turns out that this supposition
is dead wrong. A statistical model basically just works better than a rule-based
approach. As a side benefit, a Graham-style Bayesian filter is also simpler
and faster than SpamAssassin.

There are some issues of data structures and storage techniques that will effect
operating speed of different tools. But the actual predictive accuracy depends
on very few factors ? the main significant factor is probably the word-lexing
technique used, and this matters mostly for eliminating spurious random strings.
Barham’s implementation simply looks for relatively short, disjoint sequences
of characters in a small set (alphanumeric plus a few others).

6. Bayesian trigram filters:

Bayesian techniques built on a word model work rather well. One disadvantage
of the word model is that the number of “words” in e-mail is virtually unbounded.
This fact may be counterintuitive ? it seems reasonable to suppose that you
would reach an asymptote once almost all the English words had been included.
From my prior research into full text indexing, I know that this is simply not
true; the number of “word-like” character sequences possible is nearly unlimited,
and new text keeps producing new sequences. This fact is particularly true of
e-mails, which contain random strings in Message-IDs, content separators, UU
and base64 encodings, and so on. There are various ways to throw out words from
the model (the easiest is just to discard the sufficiently infrequent ones).

I decided to look into how well a much more starkly limited model space would
work for a Bayesian spam filter. Specifically, I decided to use trigrams for
my probability model rather than “words”.

There were several decisions I made along the way. The biggest choice was deciding
what a trigram is. While this is somewhat simpler than identifying a “word”,
the completely naive approach of looking at every (overlapping) sequence of
three bytes is non-optimal. In particular, considering high-bit characters ?
although occurring relatively frequently in multi-byte character sets (in other
words, CJK) ? forces a much bigger trigram space on us than does looking only
at the ASCII range. Limiting the trigram space even further than to low-bit
characters produces a smaller space, but not better overall results.

For my trigram analysis, I utilized only the most highly differentiating trigrams
as message categorizers. But I arrived at the chosen numbers of “spam” and “good”
trigrams only by trial and error. I also picked the cutoff probability for spam
rather arbitrarily: I made an interesting discovery that no message in the “good”
corpus was assigned a spam probability above .0071 other than two false positives
in the .99 range. Lowering my cutoff from an initial 0.9 to 0.1, however, allowed
me to catch a few more message in the “spam” corpus. For purposes of speed,
I select no more than 100 “interesting” trigrams from each candidate message
? changing that 100 to something else can produce slight variations in the
results (but not in an obvious direction).
Posted by: David at 09:08 AM
Spam: The Plague of the Internet

“Spam” is the term for unsolicited commercial bulk email, this which started out a very small nuisance on the internet has grown to become something that plagues people every time they check their inbox. This spam fills up inboxes with unsolicited mail for services that no one could ever want. They cost time and money to those that receive them and the cost to the email servers and internet service providers (ISP’s) in then passed on to the consumers in the form of higher bills. Measures have been taken to put and end to this scourge and to prevent future spammers from arising.

Spam is an ineffective way to advertise as it provides people with information about products and services that likely are of use to no one. Many spammers are unaware of what they are doing and have probably been duped into some kind of “Get-Rich-Quick” scheme. In a article about the commercial uses of spam (from Alchemy Mindworks) it was explained the reason that people spam and how they might be unaware of the damage they are causing:

The most prevalent sort of junk e-mail is commercial advertising. Judging by the content of most of these messages, their perpetrators have all just signed up with an Internet access provider, and were given complimentary copies of one of the many “How to Make Lots of Money on the Internet” books. Some of them are genuinely inconsiderate of the rights of other users of the net ? the bulk of them, however, are merely confused, deluded and ignorant.

These unsolicited emails take time out of someone busy day to either read or get rid off; they become a constant hassle to your everyday routine. Another way that people get on the spam lists in the first place is when spammers get a hold of mailing list for an organization or forum:

One particularly nasty variant of email spam is sending spam to mailing lists (public or private email discussion forums.) Because many mailing lists limit activity to their subscribers, spammers will use automated tools to subscribe to as many mailing lists as possible, so that they can grab the lists of addresses, or use the mailing list as a direct target for their attacks.

Spam can come from anywhere and internet users should be very careful about who they give their email address too.

Ways to get rid of spam are ever rising and slowing down the amount of junk mail that email users receive. For those who use programs to read their emails there are solutions such as SpamCop a highly rated program and the slightly over zealous Spam Hater, both programs not only stop spam but report complaints to the senders of the spam. Online email such as Hotmail also have taken measure to prevent spam in a help file one can learn,
In MSN Hotmail, you have several ways to protect yourself from junk mail: On the page, set up the Junk Mail Filter to redirect spam to your Junk Mail folder.
You can set the Junk Mail Filter to High or Exclusive, and then create a Safe List of addresses that should always send messages to your Inbox… If an offending message still gets into your Inbox, click the check box to the left of the message, and then click Block to stop future messages from that sender from entering your Inbox.

Other measures should be taken as checking out a website or forum for a privacy policy before giving away your email address and when signing up for a service be sure not to check anything that says that you would like to receive “special offers” or “important information” these can be a red flag that the service is linked to potential spammers.

Spam is a nuisance and will hopefully be eliminated or at least slowed down over time. More people learn how to stop or prevent it everyday and hopefully one day it will no longer be in existence. Until then, everyone must take measures to protect their inbox from being crammed full of emails about something no one could ever want.

Blog comment spam can´t be comprared yet with his big brother “email” but there is enough presence to be considered as a danger. MT doesn´t help too much cleaning your articles from spamming post so if you don´t want to spend an hour each day doing blog cleaning I recommend you to take action right now. They use bots to kill your blog but you don´t have a cleaning bot, remewmber this!. We must hit asap before this becomes a major problem. Some blacklists are ready to use and other methods are a good starting point. I have collected here some methods and solutions that blog owner are developing. I´ll add a brief description of each method and a link to the author´s website where you ca find more info. I don´t want to infright copyrighted material so you must get the original content from the authore´s website.

Spam prevention is easy, some quick solutions can save your Movable Type blog from the spam plague. All the solutions I have posted here are specific for MT, I´ll try to add stuff for other blog types, sorry!. With one of this methods you can avoid most of blog spamm comments, and is very simple in case of bots, let´s go!

Email spamming is much easier than blog spamming, this is our first advantage. If a spammer wants to get an email in your inbox, he only needs your email address but if a spammer wants to get inside your blog he needs some extra effort: visit your blog and find the comment script page, then submit a post. As they use bots specifically designed for base MT installations we must change this structure as much a possible in order to increase the difficult of posting.

I recommend you to start with the easiest ones, and if they don’t keep the spammers away then try to add the advanced solutions, the reward worth the effort, so take action.

Some companies engaged in legal business, advertise their products or services using spam. They can carry out its own newsletter, but more often ordered her to those companies (or individuals) who specialize in this. The attractiveness of such advertising is its relatively low cost and (presumably) a large scope of potential customers. Such unsolicited advertising may have the opposite effect, causing rejection of the recipients, and may even become synonymous with intrusive advertising, as happened with ham SPAM.

In connection with the sharp rejection of the spam recipients, and the tightening of legislation antispamovogo share of legal goods and services in the total volume of spam is decreasing.

It is believed that, with proper organization of promotional mailings, they can actually increase sales, not delivered to the recipient of special concern. The principal terms of mutual benefit to the recipient and the organizer of distribution are:

combining the roles of organizer and dispatches e-mail service provider;
improving the quality of the target audience of each specific distribution of advertising messages;
warning customers of the provider that he would send out promotional messages;
providing convenient means to block unwanted messages.

It should be noted, however, that the advertising message distributed to users with their consent, by definition, are not spam.

Advertising illegal products

With the help of spam frequently advertised products, which can not communicate in other ways ? such as pornography, counterfeit (fake) products, medicines with the restrictions on traffic in illegally obtained private information (databases), infringing software.

Nigerian letter

Sometimes spam is used to enticed out money from the recipient of the letter. The most common method is called ?Nigerian letter?, because a large number of these letters came from Nigeria. This letter contains a message that the recipient of the letter can be in any way a large sum of money, but the sender could help him in this. Then, the sender of the letter asked him to transfer some money under the guise of, for example, documentation or open an account. Vymanivanie this amount is the object of the bully.

A narrower called this type of fraud ? Bench or benches 419 (number of articles in the Criminal Code of Nigeria).
Phishing

?Phishing? (engl. phishing from fishing ? fishing) ? another way of fraud through spam. It is an attempt by spammers enticed out of the recipient’s letter of credit card numbers or passwords access to online payments. This letter is usually masked by an official communication from the administration of the bank. It states that the recipient must confirm details about yourself, or else his account will be blocked, and provides the address map (of spammers) to the form to be filled. Among the data that is required to report are present and those that need fraudsters. To ensure that the victim does not have guessed about the deception, clearing the site also simulates the design of the official site of the bank.

Other types of spam
Letters of happiness
The proliferation of political advocacy.
Mass mailing list for the withdrawal of the postal system (DoS-attack).
Mass mailing on behalf of another person in order to cause a negative attitude to it.
Mass mailing of letters containing a computer virus (for primary distribution).
Our letters of Family History (as a rule, the sick, or victims of the accident the child) with the information that for every forward letters to some Internet service provider allegedly paid to the family of the victim a sum of money ?treatment?. The purpose of this list is to collect e-mail addresses: after numerous peresylok ?all friends? in the text of the letter often contains the e-mail addresses of all whom it has been forwarded previously. And among the regular recipients may be initiated and its spammer.

There are also two types of mass mailings, which are usually not included in the category of spam, because they operate unconsciously. However, they pose the same (or even more serious) problem for network administrators and end users.
Malicious programs a certain type (e-mail worms) are distributed via email. Infect another computer, the worm scans the computer to search for e-mail addresses and sends itself to those addresses.
Some antivirus programs and spam filters, receiving contaminated letter sent to the return address of a notification of the virus (while mailing worms often substitutes as a return address randomly selected). As a result, dozens of people, not related to the dispatch, receiving reports that their computers are infected. However, this behavior may occur only with very old programs ? were released before the problem of spam on the Internet rose to his full height.