Supervised classification of spam emails with natural language stylometry. Issue 8 (November 2016)

• Record Type: Journal Article
• Authors: Shams, Rushdi; Mercer, Robert
• Is Part Of: Neural computing & applications. Volume 27:Issue 8(2016)
• Page Start: 2315
• Page End: 2331
• Publication Date: 2016-11
• Subjects: Spam classification -- Natural language processing -- Stylometry -- Supervised machine learning -- Text classification -- Computational linguistics -- Text mining -- Performance evaluation; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2069-7
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Email spam is one of the biggest threats to today's Internet. To deal with this threat, there are long-established measures like supervised anti-spam filters. In this paper, we report the development and evaluation ofsentinel —an anti-spam filter based on natural language and stylometry attributes. The performance of the filter is evaluated not only on non-personalized emails (i.e., emails collected randomly) but also on personalized emails (i.e., emails collected from particular individuals). Among the non-personalized datasets are CSDMC2010, SpamAssassin, and LingSpam, while the Enron-Spam collection comprises personalized emails. The proposed filter extracts natural language attributes from email text that are closely related to writer stylometry and generate classifiers using multiple learning algorithms. Experimental outcomes show that classifiers generated by meta-learning algorithms such asadaboostm1 andbagging are the best, performing equally well and surpassing the performance of a number of filters proposed in previous studies, while a random forest generated classifier is a close second. On the other hand, the performance of classifiers using support vector machine and Naïve Bayes is not satisfactory. In addition, we find much improved results on personalized emails and mixed results on non-personalized emails.