Predicting Phishing Websites Using Classification Mining Techniques with Experimental Case Studies

Aburrous, Maher; Dahal, Keshav; Thabtah, Fadi

doi:10.1109/itng.2010.117

Cited by 71 publications

(43 citation statements)

References 8 publications

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“…One approach employed in (8), is based on experimentally contrasting association classification algorithms, i.e. Clasification Based Association (CBA) ,and Multi-class Classification based on Association classification with other traditional clasification algorithms (C4.5, PART,…etc.).…”

Section: Related Workmentioning

confidence: 99%

Intelligent rule‐based phishing websites classification

Mohammad

Thabtah

McCluskey

2014

IET Information Security

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199

132

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Section: Related Workmentioning

confidence: 99%

Intelligent rule‐based phishing websites classification

Mohammad

Thabtah

McCluskey

2014

IET Information Security

Self Cite

199

132

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“…In this section, we review current intelligent anti-phishing approaches as well as the techniques they utilize in developing solutions. One approach employed in [13]is based on experimentally contrasting associative classification algorithms. The authors have gathered 27 different features from various websites as shown in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Predicting phishing websites based on self-structuring neural network

Mohammad

Thabtah

McCluskey

2013

Neural Comput & Applic

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261

131

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-Internet has become an essential component of our everyday social and financial activities. Nevertheless, internet-users may be vulnerable to different types of web-threats which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customer's confidence in ecommerce and online banking. Phishing is considered as a form of web-threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain confidential information such as usernames, passwords and social security number. So far, there is no single solution that can capture every phishing attack. In this article, we proposed an intelligent model for predicting phishing attacks based on Artificial Neural Network "ANN" particularly self-structuring neural networks. Phishing is a continuous problem where features significant in determining the type of webpages are constantly changing. Thus, we need to constantly improve the network structure in order to cope with these changes. Our model solves this problem by automating the process of structuring the network and shows high acceptance for noisy data, fault tolerance and high prediction accuracy. Several experiments were conducted in our research, the number of epochs differs in each experiment. From the results, we find that all produced structures have high generalization ability.

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“…Aburrous et al [6] in this paper present the novel approach to overcome the difficulty and complexity in detecting and predicting phishing website. They proposed an intelligent resilient and effective model that is based on classification and association data mining algorithm.…”

Section: Related Workmentioning

confidence: 99%

Kernel k-Means Clustering for Phishing Website and Malware Categorization

Sahu¹,

Shrivastava²

2015

IJCA

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In these days there are two famous internet attacks these are malware and phishing. Malware stands for malicious software. It is designed to damage computer system without knowledge of the user. Phishing website is comparatively new internet crime to malware attack. Phishing is a form of online fraud such as social engineering schemes by sending e-mails, sudden message or online advertising attract users to phishing website that pretend to be trustworthy website in order to trick individuals sensitive information for illustration-financial accounts, password and personal identification numbers, which is used for profit. Malware and Phishing website is share same properties, firstly increasing at a rate of thousands per day and secondly phishing webpage represented by the term frequencies of the website content share comparable characteristic of malware samples represented through instruction frequencies of the program executable code. Past few years many techniques have been develop to detect malware and phishing website. In these techniques firstly extract feature from phishing website or malware and then categorize them into group. In this paper, we proposed Kernel k-means clustering to categorize malware and phishing website. Kernel k-means is advance version of the k-means algorithm. In which vectors are mapped from vector space to a higher dimensional feature space through kernel function and then k-means is applied in feature space. Thus kernel kmeans avoids the separable clusters in vector space and improves the accuracy of phishing website and malware categorization. General TermsClustering, Internet attack.

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Predicting Phishing Websites Using Classification Mining Techniques with Experimental Case Studies

Cited by 71 publications

References 8 publications

Intelligent rule‐based phishing websites classification

Intelligent rule‐based phishing websites classification

Predicting phishing websites based on self-structuring neural network

Kernel k-Means Clustering for Phishing Website and Malware Categorization

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