Quantum based neural network classifier and its application for firewall to detect malicious web request

Abstract

In this paper, a quantum based neural network classifier is designed as a Firewall (QNN-F) to detect malicious Web requests on the Web. The proposed algorithm forms a neural network architecture constructively by adding the hidden layer neurons. The connection weight and threshold of the neurons are decided using the quantum computing concept. The quantum computing concept gives large subspace for selection of appropriate connection weights in evolutionary ways. Also, the threshold value is decided using the quantum computing concept. To enhance the performance of the system, a Web crawler is also proposed which finds objectionable URLs on the Web according to the objectionable keywords. The proposed algorithm is tested on Web data, to develop a firewall which detects malicious Web requests. Extensive testing on 2000 objectionable and non objectionable URLs are done which shows that proposed system works efficiently for detection of objectionable content. To judge the performance of the proposed classifier, it is compared with the Support Vector Machine, Back Propagation neural learning algorithm and quantum based classifier (Q-BNN). The comparison validates that, the QNN-F performs better than other compared algorithms.