LENS LEveraging anti-social Networking against Spam (Introduction) MSc. Sufian Hameed Dr. Pan Hui...
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Transcript of LENS LEveraging anti-social Networking against Spam (Introduction) MSc. Sufian Hameed Dr. Pan Hui...
LENSLEveraging anti-social Networking against Spam
(Introduction)
MSc. Sufian Hameed
Dr. Pan Hui
Prof. Xiaoming Fu
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Agenda
• Introduction and Motivation• State of the Art• LENS• Experiments and Results
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1. Introduction and Motivation• Spam
– Unsolicited bulk messages sent indiscriminately
– Increased from 65% in 2005 to 81% in 2009
– 200 billion spams with avg size of 8Kbytes• Per day space consumption and bandwidth usage is 1,525,879 GB
• Common Protection Techniques– Content-Based Filtering
– Sender Authentication
– Header-Based approach
– Social Network Approach
• Problems– False positives and negative
– Spam already traversing the network
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2. State of the Art• Personal Email
– a social network of friends in the cyberspace based on the emails exchanged between them
– local clustering properties of social network classify emails
– able to classify 53% of all the emails as spam or non-spam with 100% accuracy.
– limited to offline analysis
– 47% emails are left for other filtering techniques.
• Reliable Email– Uses whitelist of friends and FoF to accept email
– Accepts 85% of the emails and prevents 88% of false positives
– Infrastructural overhead (public/private keys Attestation Server)
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3. LENS: LEveraging anti-social Networking against Spam• Anti-social networking paradigm, based on an underlying social
infrasrtucture– Extend spam protection beyond social network
– Prevent transmission of spam across the network
• Receive all legitimate emails
• Prevents all spam transmission
• LENS consists of two parts– Formation of social network .i.e. community formation
– Anti-social networking i.e. GK selection
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3.1 Community Formation
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GK Selection
SKList (SK, GKID, RNID)
SignList (Signature[(CNID)Sign-SK, GKID, RNID ])
Add to SKList
Add to SignList
Add to SKList
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GK Selection – stage 1 CommLists1
3 – F
33 – F
36 – F
32 – F
31 – F
2 – FoF – 3
4 – FoF – 33
34 – FoF – 33
35 – FoF – 36
38 – FoF – 36
37 – FoF – 32
30 – FoF – 31
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12 – F
6 – F
19 – F
17 – F
14 – F
11 – FoF – 12
10 – FoF – 6
20 – FoF – 19
18 – FoF – 17
16 – FoF – 17
13 – FoF – 14
15 – FoF – 141
SK ,5, 1
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SK ,5, 1
6
Sign[(6)SK, 5, 1]
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Sign[(19)SK, 5, 1]
SignList
SKList
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GK Selection – stage 2
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GK Selection – stage 3
Authentication
Annonce
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Email Processing
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Email processing with LENS
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4. Experiments and Results
Concerned in evaluating two things• Scalability
– OSN Date (FaceBook and Flickr)
• Effectiveness at accepting all the legitimate inbound emails.– Two real email traces (Enron and Uni-Kiel)
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OSN Data
• Interested in– # of GKs for receiving messages– Reachablity of recipient via GK
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• 4000 nodes
• Community size 100-1500
• Number of GKs– GKs between 56-880
– SKList entry in 76 bytes
– 70 Kbytes in worse case
• Reachablity of recipient via GK
• Between 710K - 1.7 million (23-54%)
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Flickr
• 4000 nodes
• Community size 100-1500
• Number of GKs– GKs between 25-397
– SKList entry in 76 bytes
– 28 Kbytes in worse case
• Reachablity of recipient via GK
• Between 682K-920K (39-54%)
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Email Data Set
• Enron– Contains data from mostly senior management of Enron.
• Uni-Kiel– Data taken from log files of the email server at Kiel University
over a period of 112 days.
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Evaluations of Email Dataset
• Email Acceptance • Number of GKs• Space
Requirement• Message
Overhead
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Thank You