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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Fine-tuning restricted Boltzmann machines using quaternions and its application for spam detection

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Author(s):
da Silva, Luis A. [1] ; da Costa, Kelton A. P. [1] ; Papa, Joao P. [1] ; Rosa, Gustavo [1] ; de Albuquerque, Victor Hugo C. [2]
Total Authors: 5
Affiliation:
[1] UNESP Sao Paulo State Univ, Sch Sci, Bauru - Brazil
[2] Univ Fortaleza, Programa Posgrad Informat Aplicada, Fortaleza, Ceara - Brazil
Total Affiliations: 2
Document type: Journal article
Source: IET NETWORKS; v. 8, n. 3, SI, p. 164-168, MAY 2019.
Web of Science Citations: 0
Abstract

Restricted Boltzmann Machines (RBMs) have been used in a number of applications, but only a few works have addressed them in the context of information security. However, such models have their performance severely affected by some hyperparameters that are usually hand-tuned. In this work, the authors consider learning features in an unsupervised fashion by means of RBMs fine-tuned by hypercomplex-based metaheuristic techniques in the context of malicious content detection. Experiments are conducted over three public datasets and six metaheuristic techniques, which are used to fine-tune RBM hyperparameters such that RBM extracts features that best represent malicious content present in spam e-mail messages, and generates a dataset to be used as input to classification through the Optimum Path Forest supervised algorithm. Experimental results demonstrate that a small number of features generated through RBM can achieve a competitive accuracy in relation to the original dataset, however, with lower computational cost. Furthermore, this study presents the power of quaternions for RBMs parameter optimisation, comparing it against the well-known Harmonic Search, as well as its variants Improved Harmonic Search and Parameter Setting-Free Harmonic Search. It was concluded that RBM-based learning techniques are suitable for features extraction in the context of this work. (AU)

FAPESP's process: 17/22905-6 - About image security using machine learning
Grantee:Kelton Augusto Pontara da Costa
Support type: Regular Research Grants