On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Mining e-mail content for author identification forensics
ACM SIGMOD Record
Machine Learning
Authorship Attribution with Support Vector Machines
Applied Intelligence
Gender-Preferential Text Mining of E-mail Discourse
ACSAC '02 Proceedings of the 18th Annual Computer Security Applications Conference
Journal of the American Society for Information Science and Technology
ACM Transactions on Information Systems (TOIS)
Using PLSI-U to detect insider threats by datamining e-mail
International Journal of Security and Networks
Mining spam email to identify common origins for forensic application
Proceedings of the 2008 ACM symposium on Applied computing
Computational methods in authorship attribution
Journal of the American Society for Information Science and Technology
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Handbook of Digital Forensics and Investigation
Handbook of Digital Forensics and Investigation
A novel approach of mining write-prints for authorship attribution in e-mail forensics
Digital Investigation: The International Journal of Digital Forensics & Incident Response
Mining writeprints from anonymous e-mails for forensic investigation
Digital Investigation: The International Journal of Digital Forensics & Incident Response
E-mail protocols with perfect forward secrecy
International Journal of Security and Networks
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We present an investigation analysis approach for mining anonymous email content. The core idea behind our approach is concentrated on collecting various effective features from previous emails for all the possible suspects. The extracted features are then used with several machine learning algorithms to extract a unique writing style for each suspect. A sophisticated comparison between the investigated anonymous email and the suspects writing styles is employed to extract evidence of the possible email sender. Extensive experimental results on a real data sets show the improved performance of the proposed method with very limited number of features.