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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Abhishek Ghag Darshan Kapadia Pratik Singh
Basics of P2P
Overview of Paper 1
Overview of Paper 2
Overview of Paper 3
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.
For Type 2 and 3 Spam
5a. Groups are ranked by cosine similarity (or some other query-dependent ranking function).
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
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.