Towards detection of phishing websites on client-side using machine learning based approach

Jain, Ankit Kumar; Gupta, Brij B.

doi:10.1007/s11235-017-0414-0

Cited by 142 publications

(80 citation statements)

References 22 publications

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“…Compared to some of the related works with very high performances (summarized in table 7), our work compares favorably (in terms of accuracy and FN) against most of them. Other works by [1], [11] and [15], with higher accuracy than ours, did not report FNs to compare with. However, our work has used different and more diversified features compared to all works, therefore it is more resilient to detection evasion, in addition to a high performance.…”

Section: Discussioncontrasting

confidence: 71%

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A Framework of New Hybrid Features for Intelligent Detection of Zero Hour Phishing Websites

Nagunwa

Naqvi

Fouad

et al. 2019

Advances in Intelligent Systems and Computing

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Existing machine learning based approaches for detecting zero hour phishing websites have moderate accuracy and false alarm rates and rely heavily on limited types of features. Phishers are constantly learning their features and use sophisticated tools to adopt the features in phishing websites to evade detections. Therefore, there is a need for continuous discovery of new, robust and more diverse types of prediction features to improve resilience against detection evasions. This paper proposes a framework for predicting zero hour phishing websites by introducing new hybrid features with high prediction performances. Prediction performance of the features was investigated using eight machine learning algorithms in which Random Forest algorithm performed the best with accuracy and false negative rates of 98.45% and 0.73% respectively. It was found that domain registration information and webpage reputation types of features were strong predictors when compared to other feature types. On individual features, webpage reputation features were highly ranked in terms of feature importance weights. The prediction runtime per webpage measured at 7.63s suggest that our approach has a potential for real time applications. Our framework is able to detect phishing websites hosted in either compromised or dedicated phishing domains.

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Section: Discussioncontrasting

confidence: 71%

“…By combining Gradient Boosting DT, XGBoost and LightGBM algorithms, the model obtained an accuracy, misclassification rate and FN of 97.3%, 4.46% and 1.61% respectively. Jain et al [11], similar to [10], did not use third party features to avoid network overheads so as to improve efficiency.…”

Section: Related Workmentioning

confidence: 99%

A Framework of New Hybrid Features for Intelligent Detection of Zero Hour Phishing Websites

Nagunwa

Naqvi

Fouad

et al. 2019

Advances in Intelligent Systems and Computing

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“…5 4 24,520 138,925 99.78% (Zhao and Hoi, 2013) Classic Perceptron 990,000 10,000 99.49% (Patil and Patil, 2018) Random Forest 26,041 26,041 99.44% (Zhao and Hoi, 2013) Label Efficient Perceptron 990,000 10,000 99.41% (Chen et al, 2014) Logistic Regression 1,945 404 99.40% (Cui et al, 2018) SVM 24,520 138,925 99.39% (Patil and Patil, 2018) Fast Decision Tree Learner REPTree26,041 26,041 99.19% (Zhao and Hoi, 2013) Cost-sensitive Perceptron 990,000 10,000 99.18% (Patil and Patil, 2018) C A R T 5 26,041 26,041 99.15% (Jain and Gupta, 2018b) Random Forest 2,141 1,918 99.09% (Patil and Patil, 2018) J 4 8 6 26,041 26,041 99.03% (Verma and Dyer, 2015) J48 11,271 13,274 99.01% (Verma and Dyer, 2015) P A R T 7 11,271 13,274 98.98% (Verma and Dyer, 2015) Random Forest 11,271 13,274 98.88% (Shirazi et al, 2018) Gradient Boosting 1,000 1,000 98,78% (Cui et al, 2018) Naïve-Bayes 24,520 138,925 98,72% (Cui et al, 2018) C4.5 356,215 2,953,700 98.70% (Patil and Patil, 2018) Alternating Decision Tree 26,041 26,041 98.48% (Shirazi et al, 2018) SVM (Linear) 1,000 1,000 98,46% (Shirazi et al, 2018) CART 1,000 1,000 98,42% (Adebowale et al, 2019) Adaptive Neuro-Fuzzy Inference System 6,843 6,157 98.30% (Vanhoenshoven et al, 2016) Random Forest 1,541,000 759,000 98.26% (Jain and Gupta, 2018b) Logistic Regression 2,141 1,918 98.25% (Patil and Patil, 2018) Random Tree 26,041 26,041 98.18% (Shirazi et al, 2018) k-Nearest Neighbuors 1,000 1,000 98,05% (Vanhoenshoven et al, 2016) Multi Layer Perceptron 1,541,000 759,000 97.97% (Verma and Dyer, 2015) Logistic Regression 11,271 13,274 97.70% (Jain and Gupta, 2018b) Naïve-Bayes 2,141 1,918 97.59% (Vanhoenshoven et al, 2016) k-Nearest Neighbours 1,541,000 759,000 97.54% (Shirazi et al, 2018) SVM (Gaussian) 1,000 1,000 97,42% (Vanhoenshoven et al, 2016) C 5 . 0 8 1,541,000 759,000 97.40%…”

Section: Referencementioning

confidence: 99%

Comparison of Classification Algorithms for Detection of Phishing Websites

Vaitkevicius¹,

Marcinkevičius²

2020

Informatica

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Phishing activities remain a persistent security threat, with global losses exceeding 2.7 billion USD in 2018, according to the FBI's Internet Crime Complaint Center. In literature, different generations of phishing websites detection methods have been observed. The oldest methods include manual blacklisting of known phishing websites' URLs in the centralized database, but they have not been able to detect newly launched phishing websites. More recent studies have attempted to solve phishing websites detection as a supervised machine learning problem on phishing datasets, designed on features extracted from phishing websites' URLs. These studies have shown some classification algorithms performing better than others on differently designed datasets but have not distinguished the best classification algorithm for the phishing websites detection problem in general. The purpose of this research is to compare classic supervised machine learning algorithms on all publicly available phishing datasets with predefined features and to distinguish the best performing algorithm for solving the problem of phishing websites detection, regardless of a specific dataset design. Eight widely used classification algorithms were configured in Python using the Scikit Learn library and tested for classification accuracy on all publicly available phishing datasets. Later, classification algorithms were ranked by accuracy on different datasets using three different ranking techniques while testing the results for a statistically significant difference using Welch's T-Test. The comparison results are presented in this paper, showing ensembles and neural networks outperforming other classical algorithms.

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“…The authors in [40] introduced an anti-phishing method, which utilized several different ML algorithms and nineteen features to distinguish phishing websites from legitimate ones. The authors claimed that their model achieved a 99.39% true positive rate.…”

Section: Phishing and Spam Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.

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Towards detection of phishing websites on client-side using machine learning based approach

Cited by 142 publications

References 22 publications

A Framework of New Hybrid Features for Intelligent Detection of Zero Hour Phishing Websites

A Framework of New Hybrid Features for Intelligent Detection of Zero Hour Phishing Websites

Comparison of Classification Algorithms for Detection of Phishing Websites

Artificial Intelligence in the Cyber Domain: Offense and Defense

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