Spam detection in p2p systems
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SPAM DETECTION IN P2P SYSTEMS. Team Matrix Abhishek GhagDarshan Kapadia Pratik Singh. AGENDA. REFRESHER SOFTWARE DESIGN PROGRESS DEMO. REFRESHER. Basics of P2P Overview of Paper 1 Overview of Paper 2 Overview of Paper 3 Proposal References. Software Design.

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SPAM DETECTION IN P2P SYSTEMS

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SPAM DETECTION IN P2P SYSTEMS

Team Matrix

Abhishek GhagDarshan Kapadia Pratik Singh


AGENDA

REFRESHER

SOFTWARE DESIGN

PROGRESS

DEMO


REFRESHER

Basics of P2P

Overview of Paper 1

Overview of Paper 2

Overview of Paper 3

Proposal

References


Software Design


Working Of Napster

  • Centralized Server and a pool of clients.

  • Clients register themselves.

  • Server obtains IP Address and list of files.

  • When clients queries, server returns IP address of peers.

  • Direct downloading from peer.


Progress

  • Detailed study of structure and working of Napster systems.

  • Try to build our own system based on the study.

  • Studied the algorithm for Spam detection in detail.


Query Processing

1 Client writes a query.

2Server compares the query with its own files

3On match server returns System Identifier and Descriptor.

4 The client groups the individual groups by keys.

5 The Client ranks according to some ranking function.

6The client download the file and becomes the server.


Algorithm for Spam Detection

For Type 2 and 3 Spam

5a. Groups are ranked by cosine similarity (or some other query-dependent ranking function).


For Type 1 and 4 Spam

5b. Identify the top-M results as candidate results.

5c. Re-rank the top-M results by either NumUniqueTerms or

Jaccard/Cosine distance. The results that are low in the order are

more likely to be Type 1 spam than those higher up.

5d. Identify the top-N results, where N < M as the new candidate

results.

5e. Re-rank the top-N results by their per-host file replication

degree. The results that are low in the order are more likely to be

Type 4 spam than those higher up.


Demo


QUESTIONS???


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