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SPAM DETECTION IN P2P SYSTEMS

SPAM DETECTION IN P2P SYSTEMS. Team Matrix Abhishek Ghag Darshan 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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  1. SPAM DETECTION IN P2P SYSTEMS Team Matrix Abhishek Ghag Darshan Kapadia Pratik Singh

  2. AGENDA REFRESHER SOFTWARE DESIGN PROGRESS DEMO

  3. REFRESHER Basics of P2P Overview of Paper 1 Overview of Paper 2 Overview of Paper 3 Proposal References

  4. Software Design

  5. 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.

  6. 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.

  7. Query Processing 1 Client writes a query. 2 Server compares the query with its own files 3 On 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. 6 The client download the file and becomes the server.

  8. Algorithm for Spam Detection For Type 2 and 3 Spam 5a. Groups are ranked by cosine similarity (or some other query-dependent ranking function).

  9. 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.

  10. Demo

  11. QUESTIONS???

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