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- Title
LEARNING SPAM FEATURES USING RESTRICTED BOLTZMANN MACHINES.
- Authors
da Silva, Luis Alexandre; da Costa, Kelton Augusto Pontara; Ribeiro, Patricia Bellin; de Rosa, Gustavo Henrique; Papa, João Paulo
- Abstract
Nowadays, spam detection has been one of the foremost machine learning-oriented applications in the context of security in computer networks. In this work, we propose to learn intrinsic properties of e-mail messages by means of Restricted Boltzmann Machines (RBMs) in order to identity whether such messages contain relevant (ham) or non-relevant (spam) content. The main idea contribution of this work is to employ Harmony Search-based optimization techniques to fine-tune RBM parameters, as well as to evaluate their robustness in the context spam detection. The unsupervised learned features are then used to feed the Optimum-Path Forest classifier, being the original features extracted from e-mail content and compared against the new ones. The results have shown RBMs are suitable to learn features from e-mail data, since they obtained favorable results in the datasets considered in this work.
- Subjects
MAXWELL-Boltzmann distribution law; LEARNING; TEXT messaging spam; MATHEMATICAL optimization; BIG data
- Publication
IADIS International Journal on Computer Science & Information Systems, 2016, Vol 11, Issue 1, p99
- ISSN
1646-3692
- Publication type
Article