Artificial Neural Network for Websites Classification with Phishing Characteristics

Ferreira, Ricardo; Martiniano, Andréa; Napolitano, Domingos Márcio Rodrigues; Romero, Marcio; Gatto, Dacyr Dante de Oliveira; Farias, Edquel Bueno Prado; Sassi, Renato José

doi:10.4236/sn.2018.72008

Cited by 26 publications

(7 citation statements)

References 22 publications

(14 reference statements)

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“…In addition, a comprehensive review of existing methods deployed for the detection of phishing activities is discussed. As revealed by Ferreira et al [14], phishing was described as a technique for perpetrating online fraud by criminals through the usage of the Internet. Criminals sought to steal personal information, security credentials, and even bank details and password fraudulently by employing phishing techniques.…”

Section: Related Workmentioning

confidence: 99%

AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites

et al. 2020

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Phishing is a type of social web-engineering attack in cyberspace where criminals steal valuable data or information from insensitive or uninformed users of the internet. Existing countermeasures in the form of anti-phishing software and computational methods for detecting phishing activities have proven to be effective. However, new methods are deployed by hackers to thwart these countermeasures. Due to the evolving nature of phishing attacks, the need for novel and efficient countermeasures becomes crucial as the effect of phishing attacks are often fatal and disastrous. Artificial Intelligence (AI) schemes have been the cornerstone of modern countermeasures used for mitigating phishing attacks. AI-based phishing countermeasures or methods possess their shortcomings particularly the high false alarm rate and the inability to interpret how most phishing methods perform their function. This study proposed four (4) meta-learner models (AdaBoost-Extra Tree (ABET), Bagging -Extra tree (BET), Rotation Forest -Extra Tree (RoFBET) and LogitBoost-Extra Tree (LBET)) developed using the extra-tree base classifier. The proposed AI-based meta-learners were fitted on phishing website datasets (currently with the newest features) and their performances were evaluated. The models achieved a detection accuracy not lower than 97% with a drastically low false-positive rate of not more 0.028. In addition, the proposed models outperform existing MLbased models in phishing attack detection. Hence, we recommend the adoption of meta-learners when building phishing attack detection models.

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Section: Related Workmentioning

confidence: 99%

AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites

et al. 2020

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“…To evaluate the performance of the proposed models, assessments with the existing state-of-art techniques are necessary. As discussed in Section 2, several models such as ANN [6], SMO [35], Stacking [60], SVM [34], KNN [34], Hybrid NBTree [60] etc., have been proposed. However, from Table 7, it is evident that the proposed method (BT-FT-2) shows noteworthy improvements in almost all evaluation metrics on Dataset 1.…”

Section: Comparison Of Proposed Approaches With Existing Methodsmentioning

confidence: 99%

“…The website phishing attack is a typical example of these cyber-attacks. Nowadays, illegitimate websites are created by cyber-criminals to steal sensitive information from unsuspecting users for illegal activities [6]. The website phishing attack is a serious cybersecurity problem that overwhelms cyberspace and has a devastating effect on internet users and web-based businesses [7,8].…”

Section: Introductionmentioning

confidence: 99%

Improving the phishing website detection using empirical analysis of Function Tree and its variants

Balogun

Adewole

Raheem

et al. 2021

Heliyon

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The phishing attack is one of the most complex threats that have put internet users and legitimate web resource owners at risk. The recent rise in the number of phishing attacks has instilled distrust in legitimate internet users, making them feel less safe even in the presence of powerful antivirus apps. Reports of a rise in financial damages as a result of phishing website attacks have caused grave concern. Several methods, including blacklists and machine learning-based models, have been proposed to combat phishing website attacks. The blacklist antiphishing method has been faulted for failure to detect new phishing URLs due to its reliance on compiled blacklisted phishing URLs. Many ML methods for detecting phishing websites have been reported with relatively low detection accuracy and high false alarm. Hence, this research proposed a Functional Tree (FT) based metalearning models for detecting phishing websites. That is, this study investigated improving the phishing website detection using empirical analysis of FT and its variants. The proposed models outperformed baseline classifiers, meta-learners and hybrid models that are used for phishing websites detection in existing studies. Besides, the proposed FT based meta-learners are effective for detecting legitimate and phishing websites with accuracy as high as 98.51% and a false positive rate as low as 0.015. Hence, the deployment and adoption of FT and its metalearner variants for phishing website detection and applicable cybersecurity attacks are recommended.

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“…Using the data set from [9], Ferreira et al [10] proposed an ANN model for phishing detection. The data set contained 30 features and 11,050 records.…”

Section: Non-url Exclusivementioning

confidence: 99%

Robust URL Phishing Detection Based on Deep Learning

2020

KSII TIIS

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Phishing websites can have devastating effects on governmental, financial, and social services, as well as on individual privacy. Currently, many phishing detection solutions are evaluated using small datasets and, thus, are prone to sampling issues, such as representing legitimate websites by only high-ranking websites, which could make their evaluation less relevant in practice. Phishing detection solutions which depend only on the URL are attractive, as they can be used in limited systems, such as with firewalls. In this paper, we present a URL-only phishing detection solution based on a convolutional neural network (CNN) model. The proposed CNN takes the URL as the input, rather than using predetermined features such as URL length. For training and evaluation, we have collected over two million URLs in a massive URL phishing detection (MUPD) dataset. We split MUPD into training, validation and testing datasets. The proposed CNN achieves approximately 96% accuracy on the testing dataset; this accuracy is achieved with URL schemes (such as HTTP and HTTPS) removed from the URL. Our proposed solution achieved better accuracy compared to an existing state-of-the-art URL-only model on a published dataset. Finally, the results of our experiment suggest keeping the CNN up-to-date for better results in practice.

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Artificial Neural Network for Websites Classification with Phishing Characteristics

Cited by 26 publications

References 22 publications

AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites

AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites

Improving the phishing website detection using empirical analysis of Function Tree and its variants

Robust URL Phishing Detection Based on Deep Learning

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