Intelligent rule‐based phishing websites classification

Mohammad, Rami Mustafa A.; Thabtah, Fadi; McCluskey, T.L.

doi:10.1049/iet-ifs.2013.0202

Cited by 207 publications

(156 citation statements)

References 16 publications

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“…-IP address ( f 1 ): almost all the literatures [8], [21], [23]- [25] [24], [25] followed and used this rule to define the value of this feature. However, this rule was highly affected by the samples.…”

Section: Feature Vectormentioning

confidence: 99%

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Application of Feature Engineering for Phishing Detection

Zhang

Ren

Jiang

2016

IEICE Trans. Inf. & Syst.

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SUMMARYPhishing attacks target financial returns by luring Internet users to exposure their sensitive information. Phishing originates from e-mail fraud, and recently it is also spread by social networks and short message service (SMS), which makes phishing become more widespread. Phishing attacks have drawn great attention due to their high volume and causing heavy losses, and many methods have been developed to fight against them. However, most of researches suffered low detection accuracy or high false positive (FP) rate, and phishing attacks are facing the Internet users continuously. In this paper, we are concerned about feature engineering for improving the classification performance on phishing web pages detection. We propose a novel anti-phishing framework that employs feature engineering including feature selection and feature extraction. First, we perform feature selection based on genetic algorithm (GA) to divide features into critical features and non-critical features. Then, the non-critical features are projected to a new feature by implementing feature extraction based on a two-stage projection pursuit (PP) algorithm. Finally, we take the critical features and the new feature as input data to construct the detection model. Our anti-phishing framework does not simply eliminate the non-critical features, but considers utilizing their projection in the process of classification, which is different from literatures. Experimental results show that the proposed framework is effective in detecting phishing web pages.

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Section: Feature Vectormentioning

confidence: 99%

“…If the URL is ranked more than 1000000 or has no traffic rank, it is taken as phishing. -Hiding suspicious link ( f 14 ): a fake URL is displayed in the status bar of browser to the Internet users by using JavaScript [24]. This feature is obtained by checking HTML source code.…”

Section: Feature Vectormentioning

confidence: 99%

Application of Feature Engineering for Phishing Detection

Zhang

Ren

Jiang

2016

IEICE Trans. Inf. & Syst.

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“…Phishers redirect the user's submitted information by hiding the suspicious part of the URL to differentiate between legitimate and phishing URLs based on the URL length, Mohammad et al in [4] proposed that if the length of URL is more than 54 symbols the URL is classified phishy.…”

Section: Url Lengthmentioning

confidence: 99%

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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“…So for Alexa rank the threshold is set to 150000. If the Alexa rank of URL exceeds this threshold value then it would be classified as phishing [18].…”

Section: Alexa Rankmentioning

confidence: 99%

Phishing URL Detection: A Machine Learning and Web Mining-based Approach

Sananse¹,

Sarode²

2015

IJCA

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There has been an abrupt development and use of online transactions over the past decade. The increased sophistication of cyber criminals has lead to proliferation of phishing attacks. The continuous expansion of World Wide Web has led to the rapid spread of phishing, malware and spamming. This paper proposes a feature based approach to classify URLs into phishing or non-phishing category. The usage of a variety of URL features is done by studying the anatomy of URLs. For classification of URLs, two different algorithms have been used. Random Forest machine learning algorithm is used to build an efficient classifier which would decide whether a given URL is phishing or not. In addition, a novel scheme has been proposed to detect phishing URLs by mining the publicly available content on the URLs.

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Intelligent rule‐based phishing websites classification

Cited by 207 publications

References 16 publications

Application of Feature Engineering for Phishing Detection

Application of Feature Engineering for Phishing Detection

Phishing Websites Classification using Hybrid SVM and KNN Approach

Phishing URL Detection: A Machine Learning and Web Mining-based Approach

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