A neural model in anti-spam systems

Authors:
Otávio A. S. Carpinteiro;Isaías Lima;João M. C. Assis;Antonio C. Zambroni de Souza;Edmilson M. Moreira;Carlos A. M. Pinheiro
Affiliations:
Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil;Research Group on Computer Networks and Software Engineering, Federal University of Itajubá, Itajubá, MG, Brazil
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

Citing 12
Cited 1

A general framework for parallel distributed processing

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
A Comparative Study on Feature Selection in Text Categorization

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
"In vivo" spam filtering: a challenge problem for KDD

ACM SIGKDD Explorations Newsletter
Characterizing a spam traffic

Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
An evaluation of statistical spam filtering techniques

ACM Transactions on Asian Language Information Processing (TALIP)
A LVQ-based neural network anti-spam email approach

ACM SIGOPS Operating Systems Review
Adaptive anti-spam filtering for agglutinative languages: a special case for Turkish

Pattern Recognition Letters
Canning Spam: Proposed Solutions to Unwanted Email

IEEE Security and Privacy
2005 Special Issue: Efficient information theoretic strategies for classifier combination, feature extraction and performance evaluation in improving false positives and false negatives for spam e-mail filtering

Neural Networks - 2005 Special issue: IJCNN 2005
Support vector machines for spam categorization

IEEE Transactions on Neural Networks

Automatic thesaurus construction for spam filtering using revised back propagation neural network

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper proposes the use of the multilayer perceptron model to the problem of detecting ham and spam e-mail patterns. It also proposes an intensive use of data pre-processing and feature selection methods to simplify the task of the multilayer perceptron in classifying ham and spam e-mails. The multilayer perceptron is trained and assessed on patterns extracted from the SpamAssassin Public Corpus. It is required to classify novel types of ham and spam patterns. The results are presented and evaluated in the paper.