Although it seems like the spread of
spam-unwanted junk e-mails sent to millions of people each day – is
recent problem, spam has been around as long as the Internet has. In
fact, the first documented case of spam occurred in 1978, when a
computer company sent out 400 e-mails via the Arpanet, the precursor to
the modern Internet. Now, spam e-mails account for more than two-thirds
of all the e-mails sent over the Internet, and for some unlucky uses,
spam makes up 80 percent of the messages they receive. And despite
technological innovations such as spam filters and event new legislation
designed to combat spam, the problem will not go away easily.
The reason spammers – the people who and
businesses that spread spam – are difficult to stop is that spam is so
cost effective. It costs a spammer roughly one-hundredth of a send spam,
which means that a spammer can still make a profit even with an
extremely low response rate, as low as one sale per 100,000 e-mails
sent. This low rate gives spammers a tremendous incentive to continue
sending out millions and millions and millions of e-mils, even if the
average person never purchases anything from them. With so much at
stake, spammers have gone to great lengths to avoid or defeat spam
blockers and filters.
Most spam filters rely on a fairly
primitive “fingerprinting” system. In this system, a program analyzes
several typical spam messages and identifies common features in them.
Any arriving e-mails that match these features are deleted. But the
fingerprinting defense proves quite easy for spammer to defeat. To
confuse the program, a spammer simply has to include as series of random
characters or number. The additions to the spam message change its
“fingerprint” and thus allow the spam to escape detection. And when
programmers modify the fingerprint software to look for random strings
of letter, spammers respond by including nonrandom content, such as
sports scores or stock prices, which again defeats the system.
A second possible solution takes
advantage of a computer’s limited learning abilities. So called “smart
filters” use complex algorithms, which allow them to recognize new
versions of spam messages. These filters may be initially fooled by
random characters or bogus content, but they soon learn to identify
these features. Unfortunately, spammers have learned how to avoid these
smart filters as well.
