preventing spam: today and tomorrow zane bonny vilaphong phasiname the spamsters!
Post on 20-Dec-2015
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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.
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?
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: 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