A dynamic self-structuring neural network model to combat phishing

Fadi Thabtah, Rami M. Mohammad, Thomas McCluskey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Pages72-77
Number of pages6
ISBN (Electronic)9781509006205, 9781509006199
ISBN (Print)9781509006212
DOIs
Publication statusPublished - 3 Nov 2016
Event2016 International Joint Conference on Neural Networks - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016
https://ieeexplore.ieee.org/xpl/conhome/7593175/proceeding
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7726592 (Program)

Publication series

NameIJCNN
PublisherIEEE
ISSN (Electronic)2161-4407

Conference

Conference2016 International Joint Conference on Neural Networks
Abbreviated titleIJCNN2016
CountryCanada
CityVancouver
Period24/07/1629/07/16
Internet address

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