Preventing Spam:Today and Tomorrow

Zane BonnyVilaphong PhasinameThe Spamsters!

Summary

Why Prevent Spam How is Spam Prevented What is Wrong With This Picture? What can we do? List Based Approach Algorithm Based Approach Government Legislation Who Did What and Sources Conclusions

Why Prevent Spam

Phishing Scams Red Cross Donation

Privacy Many want your personal information

Out of control 70 to 100 a day at the average office

Costly More than 10 Billion a year.

Why Prevent Spam

ANNOYING!Who likes spam in their inbox?Can you totally eliminate spam?

How is Spam Prevented

Junk E-Mail Filter – will decide to delete a message or not based on the content of the email message.

Safe Senders List – this list defines an email as safe or not. Imagine an email message that is sent through but is deleted by the spam filter. This filter tells the email program that it is safe.

Safe Recipients Lists – this list is similar to the senders list but is instead used for large groups of people.

Blocked Senders List – this is a list of the people that will be treated as junk whether they pass the filter or not.

How is Spam Prevented

Never reply to a spam Don’t click any links in a spam email Don’t use your home or business email

address Preview your messages before you open

them Disguise your email address

What is Wrong With This Picture?

Rely heavily on the userMany of these methods do not provide

automatic protection. Lists and filters are rarely used by users Even if they are utilized it takes time to be

effective What can we do to help eliminate?

What can we do?

More user friendly methods More automatic Handled more on the IT side

List: DNS Black Listing

Implementation of an old idea Black list can be formed for an individual

This is known as DNS Blacklisting Been in use since 1997 Three requirements for Blacklist

Domain Name Server List of addresses

List: DNS Black Listing

DNSBL queries First reverses ip Second appends DNSBL with reverse IP Last checks names in list

Example IP=1.2.3.4 DNSBL=bl.black.com Sent to blacklist as 4.3.2.1.bl.black.com

Policies vary from blacklist to blacklist What does the list wish to prevent? How do you find the addresses? How long?

List: DNS Black Listing

List: Challenge Response

This is an email filter in reverseAssumes that all email is spam

First mail is sent Second challenge is issued to the sender Lastly, if the sender responds then they

are white listed

List: Challenge Response

A number of problems exist Not all email can be responded to

ListservMailing lists

Also what if a spammer used a legitimate email address?

List: Bounce Messages

What is this? Send one each time a spam email is sent A few problems….

Spammers don’t careForged return addressPretty easy to tell by header if it is real or not

Algorithm: Bayesian Probability

Bayesian achieves 98%+ spam detection rate using mathematical approach.

How does it work? Uses ham files

Ham files contain legitimate email. For example:

The word “free” can be recognize within the data base files of ham.

If the word “free” spell differently the Bayesian filter will detected as spam.

Algorithm: Chung-Kwei

Named after Feng-Shui figureThis figure was a symbol of protectionChung-Kwei is designed to protect business

Part of SpamGuru package made by IBM Uses Teiresias algorithm to discover

patterns for spam-vocabulary

Algorithm: Chung-Kwei

Spam-vocabulary is what is used to filter emails before reaching end user.

White email can remove spam from the spam-vocabulary.

Query method then classifies

Government Legislation

Why come up with a fancy technique at all why not just ask Uncle Sam for help?

Consider the Do Not Call Registry Fairly effective at deterring telemarketers Legal action is available if the telemarketers do not

comply On the flip side….

Legal questions arise And constitutional questions

Who Did What?

Vilaphong…Algorithm based approachesGovernment legislationConclusion

Zane…List based approachesPowerPoint Intro

Sources Boyce, Jim. “What to do with all that spam”. Microsoft. 1 May. 2003. 14 Nov. 2007.

<http://office.microsoft.com/en-us/outlook/HA011590551033.aspx>. “DNSBL”. Wikipedia. 13 Oct. 2007. 14 Nov. 2007. <http://en.wikipedia.org/wiki/DNSBL>. Gowan, Frith. “Don't Get Lured by Phishing Scams”. Techsoup.org. 12 Dec. 2005. 14 Nov.

2007. <http://www.techsoup.org/learningcenter/internet/page4777.cfm> Orlov, Gregory. “Spam: prevention is better than cure!”. BCS. 1 Jan. 2005. 14 Nov. 2007.

<http://www.bcs.org/server.php?show=ConWebDoc.3064>. Rigoutsos, Isidore and Huynh, Tien. “Chung-Kwei: a Pattern-discovery-based System for the

Automatic Identification of Unsolicited E-mail Messages (SPAM)”. IBM Thomas J Watson Research Center. 1 Jan. 2005. 14 Nov. 2007. <http://www.ceas.cc/papers-2004/

153.pdf>. “Section 7 - Spam Prevention”. SORBS. 1 Jan. 2004. 14 Nov. 2007. <http://www.au.sorbs.net/

spamfo/prevention.shtml>. Stuart, Anne. “Canning Spam”. Inc.com. 1 May. 2003. 14 Nov. 2007. <http://www.inc.com/

articles/2003/05/25444.html>. Tenby, Susan. “Things You Can Do to Prevent Spam”. Techsoup.org. 12 Nov. 2007. 14 Nov.

2007. <http://www.techsoup.org/learningcenter/internet/page4782.cfm>. “Why Bayesian Filtering is the Most Effective Anti-Spam Technology”. GFI.com. 1 Jan. 2007.

14 Nov. 2007. <http://www.gfi.com/whitepapers/why-bayesian-filtering.pdf>

Conclusion

Have many prevention methods already implemented Most important improvement that can be made is automation Have listing methods and algorithms. algorithms tend to yield the

best results Simple lists were sufficient in past

Today Spam has evolved to a point that it requires “smarter” methods to prevent it

The prevention of spam will undoubtedly become more of issue in the future and cost business a consumers more money A fool proof prevention is unlikely

Only 100% way is Government Regulation That also has drawbacks

Questions?