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2017 | OriginalPaper | Chapter

Malwares Classification Using Quantum Neural Network

Authors : Tu Tran Anh, The Dung Luong

Published in: Advances in Information and Communication Technology

Publisher: Springer International Publishing

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Abstract

Quantum neural networks (QNNs) have been explored as one of the best approach for improving the computational efficiency of neural networks. Because of the powerful and fantastic performance of quantum computation, some researchers have begun considering the implications of quantum computation on the field of artificial neural networks (ANNs). The purpose of this paper is to introduce an application of QNNs in malwares classification. Inherently Fuzzy Feedforward Neural Networks with sigmoidal hidden units was used to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) gave a kind of fast and realistic results compared with the (ANN’s). Simulation results indicate that QNN is superior (with total accuracy of 98.245 %) than ANN (with total accuracy of 95.214 %).

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Metadata
Title
Malwares Classification Using Quantum Neural Network
Authors
Tu Tran Anh
The Dung Luong
Copyright Year
2017
Book
Advances in Information and Communication Technology

Print ISBN: 978-3-319-49072-4

Electronic ISBN: 978-3-319-49073-1

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-49073-1_37

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