An add-on to rule-based sifters for multi-recipient spam emails

Authors:
Vipul Sharma;Puneet Sarda;Swasti Sharma
Affiliations:
Department of Computer Science, University of Houston, Houston, TX;Department of Computer Science, University of Houston, Houston, TX;Computer Science Department, College of Engineering Roorkee, Roorkee, India
Venue:
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Year:
2005

Citing 3
Cited 0

Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
A guided tour to approximate string matching

ACM Computing Surveys (CSUR)
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists

Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Spam filtering technique described here targets multiple recipient Spam messages with similar email addresses. We exploit these similar patterns to create a rule-based classification system (accuracy 92%). Our technique uses the ‘TO' and ‘CC' fields to classify an email as Spam or Legitimate. We introduce certain new rules which should enhance the performance of the current filtering techniques [1][4][5]. We also introduce a novel metric to calculate the degree of similarity between a set of strings.