Phishing techniques have not only grown in number, but also in sophistication. Phishers might
have a lot of approaches and tactics to conduct a well-designed phishing attack. The targets of
the phishing attacks, which are mainly on-line banking consumers and payment service
providers, are facing substantial financial loss and lack of trust in Internet-based services. In
order to overcome these, there is an urgent need to find solutions to combat phishing attacks.
Detecting phishing website is a complex task which requires significant expert knowledge and
experience. So far, various solutions have been proposed and developed to address these
problems. Most of these approaches are not able to make a decision dynamically on whether the
site is in fact phished, giving rise to a large number of false positives. This is mainly due to
limitation of the previously proposed approaches, for example depending only on fixed black
and white listing database, missing of human intelligence and experts, poor scalability and their
timeliness.
In this research we investigated and developed the application of an intelligent fuzzy-based
classification system for e-banking phishing website detection. The main aim of the proposed
system is to provide protection to users from phishers deception tricks, giving them the ability
to detect the legitimacy of the websites. The proposed intelligent phishing detection system
employed Fuzzy Logic (FL) model with association classification mining algorithms. The
approach combined the capabilities of fuzzy reasoning in measuring imprecise and dynamic
phishing features, with the capability to classify the phishing fuzzy rules. Different phishing experiments which cover all phishing attacks, motivations and deception
behaviour techniques have been conducted to cover all phishing concerns. A layered fuzzy
structure has been constructed for all gathered and extracted phishing website features and
patterns. These have been divided into 6 criteria and distributed to 3 layers, based on their attack
type. To reduce human knowledge intervention, Different classification and association
algorithms have been implemented to generate fuzzy phishing rules automatically, to be
integrated inside the fuzzy inference engine for the final phishing detection.
Experimental results demonstrated that the ability of the learning approach to identify all
relevant fuzzy rules from the training data set. A comparative study and analysis showed that
the proposed learning approach has a higher degree of predictive and detective capability than
existing models. Experiments also showed significance of some important phishing criteria like
URL & Domain Identity, Security & Encryption to the final phishing detection rate.
Finally, our proposed intelligent phishing website detection system was developed, tested and
validated by incorporating the scheme as a web based plug-ins phishing toolbar. The results
obtained are promising and showed that our intelligent fuzzy based classification detection
system can provide an effective help for real-time phishing website detection. The toolbar
successfully recognized and detected approximately 92% of the phishing websites selected from
our test data set, avoiding many miss-classified websites and false phishing alarms.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/4873 |
| Date | January 2010 |
| Creators | Abur-rous, Maher Ragheb Mohammed |
| Contributors | Hossain, M. Alamgir, Dahal, Keshav P., Thabtah, Fadi |
| Publisher | University of Bradford, Department of Computing |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Thesis, doctoral, PhD |
| Rights | <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>. |
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