Protect sensitive sites from phishing attacks using features extractable from inaccessible phishing URLs

Chu, Weibo; Zhu, Bin; Feng, Xue; Guan, Xiaohong; Cai, Zhongmin

doi:10.1109/icc.2013.6654816

Cited by 44 publications

(45 citation statements)

References 13 publications

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“…W. Chu et el. [6] presented a machine learning based phishing detector using only lexical and domain features, which are available even when the phishing webpages are inaccessible (since many phishing Webpages had a very short life span). They investigated the effectiveness of each feature, and each group of features on classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Two feature combinations achieve the best overall accuracy of 94.87% for SVM classifier. The first comprises of the following 16 features: 2, 6,7,8,9,11,13,14,15,19,20,24,25,26,27,28. The second comprises of the same 16 features in the first combination except replacing the Redirect feature with popUpWidnow.…”

Section: Evaluation With Omitting Redundant Featuresmentioning

confidence: 99%

“…Table 5 shows the most effective subset of features selected by wrapper method that results in the best performance for each classifier. The feature combination that achieves the best overall accuracy of 94.67 % for decision tree classifier comprises of the following 12 features (listed by their index in Table 1): 2, 6,7,8,9,11,14,15,24,25,26,30. The feature combination that achieves the best overall accuracy of 93.93% for Naive Bayes classifier compromises of the following 10 features: 2, 6, 7, 8, 9, 14, 16, 26, 28, 29. The feature combination that achieves the best overall accuracy of 94.54% for SVM classifier compromises of the following 18 features: 1, 2, 3, 5, 6, 7, 8, 10, 13, 14, 16, 19, 21, 22, 26, 27, 28, 30. …”

Section: Evaluation With Omitting Redundant Featuresmentioning

confidence: 99%

“…The feature combination that achieves the best overall accuracy of 95.40% for decision tree classifier comprises of the following 14 features (listed by their index in Table 1) : 6, 7, 8, 9, 10, 13, 14, 15, 20, 24, 25, 26, 27, 28. The feature combination that achieves the best overall accuracy of 94.055% for Naive Bayes classifier comprises of the following 16 features: 2, 3,6,7,8,9,11,13,14,15,20,24,25,26,27,28. Two feature combinations achieve the best overall accuracy of 94.87% for SVM classifier.…”

Section: Evaluation With Omitting Redundant Featuresmentioning

confidence: 99%

“…These features are commonly divided into two categories: URL-based [16,5,6] features and content-based features [30,27]. The former, refers to the URL patterns of phishing sites that distinguish them from benign ones.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

On Determining the Most Effective Subset of Features for Detecting Phishing Websites

Hassan¹

2015

IJCA

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Phishing websites are a form of mimicking the legitimate ones for the purpose of stealing user 's confidential information such as usernames, passwords and credit card information. Recently machine learning and data mining techniques have been a promising approach for detection of phishing websites by distinguishing between phishing and legitimate ones. The detection process in this approach is preceded by extracting various features from a website dataset to train the classifier to correctly identify phishing sites. However, not all extracted features are effective in classification or equivalent in their contribution to its performance. In this paper, we investigate the effect of feature selection on the performance of classification for predicting phishing sites. We evaluate various machine learning algorithms using a number of feature subsets selected from an extracted feature set by various feature selection techniques in order to determine the most effective subset of features that results in best classification performance. Empirical results shows that using our new proposed methodology for selecting features by removing redundant ones that equally contribute to the classification accuracy, the decision tree classifier achieves the best performance with an overall accuracy of 95.40%, false positive rate (FPR) of 0.046 and false negative rate (FNR) of 0.065.

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Section: Related Workmentioning

confidence: 99%

Section: Evaluation With Omitting Redundant Featuresmentioning

confidence: 99%

Section: Evaluation With Omitting Redundant Featuresmentioning

confidence: 99%

Section: Evaluation With Omitting Redundant Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On Determining the Most Effective Subset of Features for Detecting Phishing Websites

Hassan¹

2015

IJCA

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show abstract

Learning Tensor-Based Representations from Brain-Computer Interface Data for Cybersecurity

Rahman

Bardhan

Neupane

et al. 2019

Machine Learning and Knowledge Discovery in Databases

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Using a Machine Learning Model for Malicious URL Type Detection

Tung

Wong

Kuzminykh

et al. 2022

Lecture Notes in Computer Science

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The world wide web, beyond its benefits, has also become a major platform for online criminal activities. Traditional protection methods against malicious URLs, such as blacklisting, remain a valid alternative, but cannot detect unknown sites, hence new methods are being developed for automatic detection, using machine learning approaches. This paper strengthens the existing state of the art by proposing an alternative machine learning approach, that uses a set of 14 lexical and host-based features but focuses on the typical mechanisms employed by malicious URLs. The proposed method employs random forest and decision tree as core mechanisms and is evaluated on a combined benign and malicious URL dataset, which indicates an accuracy of over 97%.

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Protect sensitive sites from phishing attacks using features extractable from inaccessible phishing URLs

Cited by 44 publications

References 13 publications

On Determining the Most Effective Subset of Features for Detecting Phishing Websites

On Determining the Most Effective Subset of Features for Detecting Phishing Websites

Learning Tensor-Based Representations from Brain-Computer Interface Data for Cybersecurity

Using a Machine Learning Model for Malicious URL Type Detection

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