Detection of phishing attacks using probabilistic neural network with a novel training algorithm for reduced Gaussian kernels and optimal smoothing parameter adaptation for mobile web services
by S. Priya; S. Selvakumar
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 36, No. 2, 2021

Abstract: The rapid escalation of smartphones and online transactions increases the rate of phishing attacks that exploit the user credentials for fraudulent financial gains. The existing detection methods suffer from low detection accuracy and high false positive rate (FPR). In this study, the probabilistic neural network (PNN) with a novel training algorithm is used for detecting phishing attacks. A novel fuzzy dense K-modes (FDKM) clustering algorithm is proposed for obtaining the Gaussian kernels in pattern layer. Moreover, the proposed optimisation procedure called modified harmony search with generation regrouping (MHS_GR) finds the optimal smoothing parameter for training the network. The proposed approach was evaluated on benchmark phishing datasets obtained from UCI machine learning repository and on our Phish_Net dataset. The experimental results reveal that the proposed PNN with MHS_GR (PNN_HS3) obtained 98.53%, 96.92%, and 97.12% of detection accuracy and 2.02%, 3.39%, and 3.12% of FPR for UCI_1, UCI_2, and Phish_Net dataset respectively.

Online publication date: Tue, 02-Mar-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com