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  1. Proceedings of the AINTEC 2014 on Asian Internet Engineering Conference (AINTEC '14)
  2. Clustering Spam Campaigns with Fuzzy Hashing
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On the Impact of Mobile Network Delays on Connection Establishment Performance of a Carrier Grade NAT Device
LTE Peak Data Rate Estimation Using Modified alpha-Shannon Capacity Formula
Security Analysis of Software Defined Networking Architectures: PCE, 4D and SANE
Security Analysis of Security Applications for Software Defined Networks
Wi-Pi: Distributed Wi-Fi Performance Assessment using Raspberry Pi
An Analysis of Data Dissemination on VANET by using Content Delivery Network (CDN) technique
Comparing NDN and CDN Performance for Content Distribution Service in Community Wireless Mesh Network
NetHAM-nano: A Robust and Scalable Service-Oriented Platform for Distributed Monitoring
A Comparative Analysis of Appliance Classifiers for Wireless Classification System
Clustering Spam Campaigns with Fuzzy Hashing

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Clustering Spam Campaigns with Fuzzy Hashing

Content Provider ACM Digital Library
Author Fontugne, Romain Kato, Akira Chen, Jianxing Fukuda, Kensuke
Abstract Identifying spamming botnets is essential to defeat spammers and reduce the harm caused by spam emails. The first step to uncover these botnets is the identification of spam campaigns. Simple methods looking for common identifiers in emails, such as URL or email addresses, are inefficient due to the emergence of obfuscation techniques like URL shortening. In this paper we propose a new method based on fuzzy hashing to cluster spam with common goals into the same spam campaign. Fuzzy hashing allows us to identify emails with similar contents even though usual identifiers are obfuscated. Using the proposed method we process a three year long dataset that consists of 540 thousand spam emails. The efficiency of the proposed method is assessed by inspecting the characteristics of the top 100 campaigns found. Finally, we present typical behaviors of the uncovered spam campaigns and the corresponding botnets.
Starting Page 66
Ending Page 73
Page Count 8
File Format PDF
ISBN 9781450332514
DOI 10.1145/2684793.2684803
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-26
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Botnet Clustering Spam
Content Type Text
Resource Type Article
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