Detecting Phishing Websites Using Rule-Based Classification Algorithm: A Comparison

Gautam, Sudhanshu; Rani, Kritika; Joshi, Bansidhar

doi:10.1007/978-981-10-3932-4_3

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…This model is similarly reliant on the training set's quality and quantity. Gautam et al (2018) employed a method called correlation data mining. They proposed a categorization system based on criteria for detecting phishing sites.…”

Section: Related Workmentioning

confidence: 99%

Employing cluster-based class decomposition approach to detect phishing websites using machine learning classifiers

Al-Tamimi¹,

Shkoukani²

2023

10.5267/j.ijdns

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Phishing is an attack by cybercriminals to obtain sensitive information such as account IDs, usernames, and passwords through the use of the anonymous structure of the Internet. Although software companies are launching new anti-phishing tools that use blacklists, heuristics, visual methods, and machine learning-based methods, these products cannot prevent all phishing attacks. This research offers an opportunity to increase accuracy in the detection of phishing sites. This study develops a model using machine learning algorithms, specifically the decision tree and the random forest, due to their outperforming the rest of the classifiers and being accredited by researchers in this field to achieve the highest accuracy. The study is based on two phases: the first phase is to measure the accuracy of classifiers on the dataset in the usual way before and after feature selection. The second phase uses the class decomposition approach and measures the accuracy of classifiers in the dataset before feature selection and after feature selection to detect phishing sites. The class decomposition approach is a technique to improve the performance of classifiers by distributing each class into clusters and renaming the examples of each cluster with a new class. This provides a specific metric that more accurately predicts the level of phishing. Testing on a dataset containing 11,055 instances, 4,898 phishing, and 6,157 legitimate, each instance has 30 features. It achieved the highest accuracy in the first phase through the random forest algorithm by 96.9% before feature selection, and after feature selection, it was by 97.1%. In the second phase, the highest accuracy of both the decision tree and random forest classifiers was achieved by 100% with the two and four classes after feature selection. While before feature selection, the random forest algorithm achieved 100% with only the two classes.

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Section: Related Workmentioning

confidence: 99%

Employing cluster-based class decomposition approach to detect phishing websites using machine learning classifiers

Al-Tamimi¹,

Shkoukani²

2023

10.5267/j.ijdns

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show abstract

“…Web phishing has been done via various ways such as lengthy URLs, URLs link carrying@symbol, URL modification, fake hypertext transfer protocol or SSL, pop‐up window, redirect page, website traffic, appending IP address with URLs hiding the links and etc. [33 ]. The legitimate web address possesses certain standards that can help in filtering fake websites.…”

Section: Taxonomy Of Web Phishing Detectionmentioning

confidence: 99%

Web phishing detection techniques: a survey on the state‐of‐the‐art, taxonomy and future directions

Vijayalakshmi¹,

Shalinie²,

Yang

et al. 2020

IET Networks

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Internet dragged more than half of the world's population into the cyber world. Unfortunately, with the increase in internet transactions, cybercrimes also increase rapidly. With the anonymous structure of the internet, attackers attempt to deceive the end‐users through different forms namely phishing, malware, SQL injection, man‐in‐the‐middle, domain name system tunnelling, ransomware, web trojan, and so on. Amongst them, phishing is the most deceiving attack, which exploits the vulnerabilities in the end‐users. Phishing is often done through emails and malicious websites to lure the user by posing themselves as a trusted entity. Security experts have been proposing many anti‐phishing techniques. Till today there is no single solution that is capable of mitigating all the vulnerabilities. A systematic review of current trends in web phishing detection techniques is carried out and a taxonomy of automated web phishing detection is presented. The objective of this study is to acknowledge the status of current research in automated web phishing detection and evaluate their performance. This study also discusses the research avenues for future investigation.

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Phishing URL Detection Using Machine Learning

Preeti

Nandal

Joshi

2020

Lecture Notes in Electrical Engineering

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Detecting Phishing Websites Using Rule-Based Classification Algorithm: A Comparison

Cited by 7 publications

References 2 publications

Employing cluster-based class decomposition approach to detect phishing websites using machine learning classifiers

Employing cluster-based class decomposition approach to detect phishing websites using machine learning classifiers

Web phishing detection techniques: a survey on the state‐of‐the‐art, taxonomy and future directions

Phishing URL Detection Using Machine Learning

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