Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

Barraclough, Phoebe, Sexton, Graham, Hossain, Alamgir and Aslam, Nauman (2014) Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy. International Journal of Advanced Research in Artificial Intelligence, 3 (10). pp. 16-25. ISSN 2165-4069

Text (Full text)
8 -2014-Parameter optimization for Intelligent.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (611kB) | Preview
Official URL:


Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuron-fuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field.

Item Type: Article
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 13:43
Last Modified: 01 Aug 2021 08:53

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics