LENS LEveraging 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

4 2. State of the Art Personal –a social network of friends in the cyberspace based on the s exchanged between them –local clustering properties of social network classify s –able to classify 53% of all the s as spam or non-spam with 100% accuracy. –limited to offline analysis –47% s are left for other filtering techniques. Reliable –Uses whitelist of friends and FoF to accept –Accepts 85% of the s and prevents 88% of false positives –Infrastructural overhead (public/private keys Attestation Server)

5 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 s Prevents all spam transmission LENS consists of two parts –Formation of social network.i.e. community formation –Anti-social networking i.e. GK selection

6 3.1 Community Formation

7 GK Selection SKList (SK, GK ID, RN ID ) SignList (Signature[(CN ID ) Sign-SK, GK ID, RN ID ]) Add to SKList Add to SignList Add to SKList

8 GK Selection – stage 1 CommLists 1 3 – F 33 – F 36 – F 32 – F 31 – F 2 – FoF – 3 4 – FoF – – FoF – – FoF – – FoF – – FoF – – FoF – – F 6 – F 19 – F 17 – F 14 – F 11 – FoF – – FoF – 6 20 – FoF – – FoF – – FoF – – FoF – – FoF – 14 1 SK,5, Sign[(6)SK, 5, 1] 19 Sign[(19)SK, 5, 1] SignList SKList

9 GK Selection – stage 2

10 GK Selection – stage 3 Authentication Annonce

11 Processing

12 processing with LENS

13 4. Experiments and Results Concerned in evaluating two things Scalability –OSN Date (FaceBook and Flickr) Effectiveness at accepting all the legitimate inbound s. –Two real traces (Enron and Uni-Kiel)

14 OSN Data Interested in –# of GKs for receiving messages –Reachablity of recipient via GK

15 FaceBook 4000 nodes Community size Number of GKs –GKs between –SKList entry in 76 bytes –70 Kbytes in worse case Reachablity of recipient via GK Between 710K million (23-54%)

16 Flickr 4000 nodes Community size Number of GKs –GKs between –SKList entry in 76 bytes –28 Kbytes in worse case Reachablity of recipient via GK Between 682K-920K (39-54%)

17 Data Set Enron –Contains data from mostly senior management of Enron. Uni-Kiel –Data taken from log files of the server at Kiel University over a period of 112 days.

18 Evaluations of Dataset Acceptance Number of GKs Space Requirement Message Overhead

19 Thank You