Intelligent Phishing Website Detection System using Fuzzy Techniques

Aburrous, Maher; Hossain, Mohammad Alamgir; Thabatah, Fadi; Dahal, Keshav

doi:10.1109/ictta.2008.4530019

Cited by 48 publications

(35 citation statements)

References 9 publications

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“…In [10] The proposed model is based on FL operators which is used to illustrate the website phishing factors and indicators as fuzzy variables and produces six measures and criteria's of website phishing attack dimensions with a layer structure. fuzzy logic techniques is the use of linguistic variables to represent Key Phishing Characteristic Indicators and relating website phishing possibility .This experimental results showed the significance and importance of the phishing website criteria (URL & Domain Identity) represented by layer one, and the variety influence of the phishing characteristic layers on the final phishing website rate.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Survey of Phishing Attack Techniques

Chawla¹,

Chouhan²

2014

IJCA

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Now in a day's phishing is a special type of network attack where the attacker creates a replica of an existing web page to fool users in to submitting personal, financial, transaction or password data to what they think is their service provider's website. Phishing has two techniques, deceptive phishing and malware -based phishing. Here we focus on deceptive phishing using social engineering schemes. To protect users against phishing, various anti-phishing techniques have been proposed. In this paper we have reviewed various phishing and anti-phishing methods for detecting and preventing phishing attack.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Survey of Phishing Attack Techniques

Chawla¹,

Chouhan²

2014

IJCA

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“…Maher Aburrous et al [6] presented novel approach to overcome the ‗fuzziness' in traditional website phishing risk assessment and proposed an intelligent resilient and effective model for detecting phishing websites. The proposed model is based on FL operators which is used to characterize the website phishing factors and indicators as fuzzy variables and produces six measures and criteria's of website phishing attack dimensions with a layered structure.…”

Section: Related Workmentioning

confidence: 99%

A Review of Exposure and Avoidance Techniques for Phishing Attack

Meena¹,

Kanti²

2014

IJCA

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Phishing is a novel kind of website/network attack which makes a deceitful attempt and influences the amenities or information security instead of stealing personal, financial and transactional data, etc. To preclude users or network from the phishing different techniques has been proposed and implemented.This paper, present the review of literature about the techniques offered by different researchers for exposing and avoiding from the phishing attack also discusses the advantages and limitation of the approaches.

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“…[11] 89 % phishing websites detection based on linear classifier by Xiang and Hong in [14] 89 % The proposed hybrid approach is also compared with other related approaches as show in Table 3, it is clear form Table 3 that the proposed hybrid KNN-SVM approach achieved the highest accuracy of 90.04% and performs better than the other approaches. Among the listed related works, the intelligent phishing detection approach based on Fuzzy data-mining algorithms proposed by Aburrous et al [13] achieved the lowest accuracy of 83.7%. Text based approach proposed by Zhang et al [11] and the phishing websites detection based on linear classifier proposed by Xiang and Hong [14], achieved performance close to the performance of our proposed hybrid KNN-SVM.…”

Section: Server Form Handler (Sfh)mentioning

confidence: 99%

“…Moreover, it is not affected by prior familiarity of the user about computer security techniques. Aburrous et al [13] proposed an intelligent phishing detection approach based on Fuzzy datamining algorithms, they used 27 features for phishing website detection and achieved an accuracy of 83.7%. However, the features used in their proposed approach are inadequate.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed KNN-SVM achieved the highest average recall of 89.08%, while the Naïve Bayes, Neural Network, SVM, DT and KNN achieved an average recall of 64.41%, 80.65%, 58.56%, 64.89% and 84.78%, respectively as shown in Table 2. [12] 84% An intelligent phishing detection approach based on Fuzzy data-mining algorithms by Aburrous et al [13] 83.7%.…”

Section: Server Form Handler (Sfh)mentioning

confidence: 99%

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Phishing Websites Classification using Hybrid SVM and KNN Approach

Altaher¹

2017

ijacsa

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Abstract-Phishing is a potential web threat that includes mimicking official websites to trick users by stealing their important information such as username and password related to financial systems. The attackers use social engineering techniques like email, SMS and malware to fraud the users. Due to the potential financial losses caused by phishing, it is essential to find effective approaches for phishing websites detection. This paper proposes a hybrid approach for classifying the websites as Phishing, Legitimate or Suspicious websites, the proposed approach intelligently combines the K-nearest neighbors (KNN) algorithm with the Support Vector Machine (SVM) algorithm in two stages. Firstly, the KNN was utilized as a robust to noisy data and effective classifier. Secondly, the SVM is employed as a powerful classifier. The proposed approach integrates the simplicity of KNN with the effectiveness of SVM. The experimental results show that the proposed hybrid approach achieved the highest accuracy of 90.04% when compared with other approaches.

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Intelligent Phishing Website Detection System using Fuzzy Techniques

Cited by 48 publications

References 9 publications

A Survey of Phishing Attack Techniques

A Survey of Phishing Attack Techniques

A Review of Exposure and Avoidance Techniques for Phishing Attack

Phishing Websites Classification using Hybrid SVM and KNN Approach

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