Intelligent phishing detection parameter framework for E-banking transactions based on Neuro-fuzzy

Barraclough, Phoebe, Sexton, Catherine, Hossain, Alamgir and Aslam, Nauman (2014) Intelligent phishing detection parameter framework for E-banking transactions based on Neuro-fuzzy. In: Proceedings of 2014 Science and Information Conference. IEEE, pp. 545-555. ISBN 9780989319317

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Phishing attacks have become more sophisticated in web-based transactions. As a result, various solutions have been developed to tackle the problem. Such solutions including feature-based and blacklist-based approaches applying machine learning algorithms. However, there is still a lack of accuracy and real-time solution. Most machine learning algorithms are parameter driven, but the parameters are difficult to tune to a desirable output. In line with Jiang and Ma’s findings, this study presents a parameter tuning framework, using Neuron-fuzzy system with comprehensive features in order to maximize systems performance. The neuron-fuzzy system was chosen because it has ability to generate fuzzy rules by given features and to learn new features. Extensive experiments were conducted, using different feature-sets, two cross-validation methods, a hybrid method and different parameters and achieved 98.4% accuracy. Our results demonstrated a high performance compared to other results in the field. As a contribution, we introduced a novel parameter tuning framework based on a neuron-fuzzy with six feature-sets and identified different numbers of membership functions different number of epochs, different sizes of feature-sets on a single platform. Parameter tuning based on neuron-fuzzy system with comprehensive features can enhance system performance in real-time. The outcome will provide guidance to the researchers who are using similar techniques in the field. It will decrease difficulties and increase confidence in the process of tuning parameters on a given problem.

Item Type: Book Section
Uncontrolled Keywords: FIS, Intelligent phishing detection, fuzzy inference system, neuro-fuzzy
Subjects: G400 Computer Science
G500 Information Systems
G700 Artificial Intelligence
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Phoebe Barraclough
Date Deposited: 09 May 2018 12:08
Last Modified: 01 Aug 2021 08:53

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