Quantum Based Neural Network Classifier and Its Application for Firewall to Detect Malicious Web Request

In this paper, a quantum based neural network classifier is designed as Firewall (QNN-F) to detect malicious Web request on the Web. The proposed algorithm forms neural network architecture constructively by adding the hidden layer neuron one by one. The connection weight and threshold of the neuron are decided using the quantum computing concept. Forming a network constructively eliminates the problem of unnecessarily learning of neural network thus save time. The quantum computing concept gives large subspace for selection of appropriate connection weight in evolutionary ways. Also, the threshold value is decided using the quantum computing concept. To increase the performance of system, a Web crawler is also proposed which find objection 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 request. 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 same dataset has been tested on well-known classifiers Support Vector Machine and Back Propagation neural learning algorithm. The comparison shows that, the QNN-F performs better than other compared algorithms.