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LENS LEveraging anti-social Networking against Spam (Introduction). MSc. Sufian Hameed Dr. Pan Hui Prof. Xiaoming Fu. Agenda. Introduction and Motivation State of the Art LENS Experiments and Results. 1. Introduction and Motivation. Spam - PowerPoint PPT Presentation
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LENSLEveraging anti-social Networking against Spam
(Introduction)
MSc. Sufian Hameed
Dr. Pan Hui
Prof. Xiaoming Fu
2
Agenda
• Introduction and Motivation• State of the Art• LENS• Experiments and Results
3
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
5
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