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Shades of Grey: On the effectiveness of reputation-based “blacklists”
Reporter: 林佳宜Email: [email protected]/8/16
ReferencesS. Sinha, M. Bailey, and F. Jahanian.
Shades Of Grey: On the effectiveness of reputation based blacklists. In International Conference on Malicious and Unwanted Software (Malware 2008), October 2008.
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OutlineIntroductionBlacklistsApproachEvaluationConclusion
IntroductionMalicious code, or malware, executed on
compromised hostsThe host-based anti-virus software
falling woefully behind–with detection rates as low as 40%
Defenders have turned to coarse-grained, reputation-based techniques
real time blacklists
Used to block unsolicited email, or spam
Investigate a number of possible causes for this low accuracy
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Several contributionsAn investigation of email, spam, and
spam tool behavior in the context of a large academic network.
An analysis of the accuracy of four prevalent spam blacklists
A preliminary study of the causes of inaccuracy and a discussion of the issues as they relate to reputation-based services
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Blacklists and ToolCurrently a large number of organizations
provide services for spam detection NJABL SORBS SpamHaus SpamCop
Using a spam detector for spam detection called SpamAssassin
These techniques have gained prominence, little is known about their effectiveness or potential
draw backs
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SpamAssassin evaluationFalse positive rate for
SpamAssassin is small close to 0.5% for a threshold of 5.0
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Experience Data Identify the spam received by a
large academic network consisting
7,000 unique hosts total of 1,074,508 emails millions of email messages, over a period 10
days in June of 2008
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Number of mails per hour observedOn an average
8,000 SMTP connections per hour
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Email characteristicsTotal of 1,074,508 emails were
successfully delivered
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Blacklists effectivenessEvaluate the false positive and false
negative rates of four blacklists NJABL, SORBS, SpamCop, SpamHaus
False negative NJABL had a false negative rate of 98% SpamCop had a false negative rate of 35%
False positive The NJABL has the least false positives The SORBS has the most false positives
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False positive rateThe NJABL has the least false
positives followed by SpamHausSORBS has an overall false positive
rate of 9.5%
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Exploring blacklist false positivesFalse positive rates for SORBS were
significantly higher. Two possible reasons:
SpamAssassin is itself wrong and the blacklists are correctly pointing out the spam.
it is likely that prominent mail servers shared by legitimate and illegitimate people are getting blacklisted
SpamAssassin we found that SpamAssassin has around 5% of false negatives
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False negative rateNJABL had a very few false
positives, it has a huge false negative
SpamCop has the smallest false negative rate
The blacklists seem to have significantly higher false
negative
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Exploring blacklist false negativesIt is difficult to come up with reasons
We do not know have access to the spamtrap deployment
We do not know the precise algorithm used for blacklisting
Two possible causes: lack of visibility and the possibility of low volume or low rate
spammers.
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Low volume/short lived spammersReason that a blacklist may miss
spam Visibility, low volume or short lived 80% of these sources were observed just for
a second
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Evaluate the coverage of different blacklistsNJABL has been omitted because
its low detection rate
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ConclusionPresented a preliminary evaluation of
four popular blacklists on an academic network
Found that the blacklists have significant false negative rates and a higher than expected false positive rate
Not be able to detect low volume spammers
Questions
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