Comparative Evaluation of Techniques for Detection of Phishing URLs

Osho, Oluwafemi; Oluyomi, Ayanfeoluwa; Misra, Sanjay; Ahuja, Ravin; Damaševičius, Robertas; Maskeliūnas, Rytis

doi:10.1007/978-3-030-32475-9_28

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…A study carried out by [36,37] showed that there are several cases of cybersecurity negligence particularly in developing countries, and proposed the need for web-based techniques for improving user side awareness of such threats. In a bid to reduce phishing attacks, existing studies have made use of machine learning techniques [38] and analyzing address-bar based features [39]. Several studies have shown significant improvements in the use of gamification methods for improving learning outcomes.…”

Section: Discussion Of Findingsmentioning

confidence: 99%

A Gamified Technique to Improve Users’ Phishing and Typosquatting Awareness

Omotosho

Awazie

Ayegba

et al. 2021

Communications in Computer and Information Science

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Security is an important aspect of technology and it requires continuous attention, but the user agent plays a crucial role in cyber risks that sensitive data are exposed to. Users' mistakes and nonchalant behavior have been found to be responsible for various breaches resulting in the increase of identity theft, extortion, loss of information and possibilities of intrusion. This paper explores the use of web-based gamification application to motivate users of the web to be cyber conscious through a fun approach designed to improve their online behavior. Results of the study shows the potentials of a gamified technique in enhancing security awareness and building more secure behaviours in users.

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Section: Discussion Of Findingsmentioning

confidence: 99%

A Gamified Technique to Improve Users’ Phishing and Typosquatting Awareness

Omotosho

Awazie

Ayegba

et al. 2021

Communications in Computer and Information Science

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“…This study examined the correlation of URL redirect chains obtained from Twitter, and then a naive Bayes classifier was used on these data, with an accuracy of 90%. An interesting comparative study was conducted by Osho et al [28] to investigate the performance of several machine learning methods for the detection of phishing websites. They found that the random forests method outperforms the existing methods, and achieves an accuracy of 97.3%.…”

Section: Machine-learning-based Detection Methodsmentioning

confidence: 99%

An Optimized Stacking Ensemble Model for Phishing Websites Detection

et al. 2021

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Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

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“…Mohammad et al contributed to the automation of the phishing URL detection task by systematically extracting URL features and proposing a hierarchical classifier according to the extraction rule [14,21]. The URL features collected and refined for phishing classification were fully exploited for 35 machine-learning-based classifiers, including the unfamiliar methods in Osho et al, and achieved a classification performance of 0.9570 based on the random forest algorithm [15].…”

Section: Related Workmentioning

confidence: 99%

Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection

Cho

2021

Electronics

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Considering the fatality of phishing attacks, the data-driven approach using massive URL observations has been verified, especially in the field of cyber security. On the other hand, the supervised learning approach relying on known attacks has limitations in terms of robustness against zero-day phishing attacks. Moreover, it is known that it is critical for the phishing detection task to fully exploit the sequential features from the URL characters. Taken together, to ensure both sustainability and intelligibility, we propose the combination of a convolution operation to model the character-level URL features and a deep convolutional autoencoder (CAE) to consider the nature of zero-day attacks. Extensive experiments on three real-world datasets consisting of 222,541 URLs showed the highest performance among the latest deep-learning methods. We demonstrated the superiority of the proposed method by receiver-operating characteristic (ROC) curve analysis in addition to 10-fold cross-validation and confirmed that the sensitivity improved by 3.98% compared to the latest deep model.

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Comparative Evaluation of Techniques for Detection of Phishing URLs

Cited by 9 publications

References 28 publications

A Gamified Technique to Improve Users’ Phishing and Typosquatting Awareness

A Gamified Technique to Improve Users’ Phishing and Typosquatting Awareness

An Optimized Stacking Ensemble Model for Phishing Websites Detection

Deep Character-Level Anomaly Detection Based on a Convolutional Autoencoder for Zero-Day Phishing URL Detection

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