A Dynamic Self-Structuring Neural Network Model to Combat Phishing

Mohammad, Rami, McCluskey, T.L. and Thabtah, Fadi Abdeljaber (2016) A Dynamic Self-Structuring Neural Network Model to Combat Phishing. In: IJCNN 2016. IEEE. ISBN 978-1-5090-0620-5

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Abstract

Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing website classification problem and it shows competitive results with respect to various evaluation measures such as harmonic mean (F1-score), precision, and classification accuracy.

Item Type:	Book Chapter
Additional Information:	a CORE "A" rated venue
Subjects:	T Technology > T Technology (General)
Schools:	School of Computing and Engineering School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
Related URLs:	Author Conference
Depositing User:	Rami Mohammad
Date Deposited:	27 Sep 2016 13:54
Last Modified:	16 Aug 2017 20:29
URI:	http://eprints.hud.ac.uk/id/eprint/29370

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• A Dynamic Self-Structuring Neural Network. (deposited 09 Jun 2016 08:40)
  • A Dynamic Self-Structuring Neural Network Model to Combat Phishing. (deposited 27 Sep 2016 13:54) [Currently Displayed]

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