Fine‐tuning restricted Boltzmann machines using quaternions and its application for spam detection. Issue 3 (1st May 2019)

• Record Type: Journal Article
• Authors: da Silva, Luis A.; da Costa, Kelton A.P.; Papa, João P.; Rosa, Gustavo; de Albuquerque, Victor Hugo C.
• Is Part Of: IET networks. Volume 8:Issue 3(2019)
• Page Start: 164
• Page End: 168
• Publication Date: 2019-05-01
• Subjects: feature extraction -- pattern classification -- Boltzmann machines -- optimisation -- learning (artificial intelligence); restricted Boltzmann machines -- quaternions -- spam detection -- Restricted Boltzmann Machines -- information security -- hand‐tuned -- unsupervised fashion -- hypercomplex‐based metaheuristic techniques -- malicious content detection -- public datasets -- RBM hyperparameters -- spam e‐mail messages -- Optimum Path Forest -- original dataset -- RBMs parameter optimisation -- Parameter Setting‐Free Harmonic Search -- RBM‐based learning techniques -- features extraction; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2018.5172
• Languages: English
• ISSNs: 2047-4954

Abstract

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.