HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks

Guo, Bingyang; Zhang, Yunyi; Xu, Chengxi; Shi, Fan; Li, Yuwei; Zhang, Min

doi:10.3390/app11209733

Cited by 7 publications

(2 citation statements)

References 41 publications

(48 reference statements)

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“…In this paper, Guo and Zhang proposed an effective phishing website detection approach called as HinPhish. 13 A heterogeneous network is constructed using relationships from webpages. This approach calculated the phish-score of the target domain on the webpage.…”

Section: Literature Surveymentioning

confidence: 99%

Phishing uniform resource locator detection using machine learning: A step towards secure system

Mahajan

2023

Security and Privacy

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The advancement in technology has led to increase in cyber‐attacks. Hackers have become more skilled at finding the loopholes in the system and can penetrate easily on to host network. The rate of cybercrimes is increasing exponentially with the growth of digital era. Phishing is considered as one of the top cybercrimes that has impacted the society at large. As per Kaspersky reports 2021, around 22% attacks were phishing attacks. This paper explores methods for detecting phishing uniform resource locator (URLs) by analyzing various features using Machine Learning techniques. Various data mining algorithms are used to learn data patterns that can identify and differentiate between benign and phishing websites using phishing website data set. The best results are shown by an XGBoost Model that provides more than 90% accuracy on the balanced class dataset.

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Section: Literature Surveymentioning

confidence: 99%

Phishing uniform resource locator detection using machine learning: A step towards secure system

Mahajan

2023

Security and Privacy

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“…Some researchers have tried to identify phishing attacks by extracting different hyperlink relationships from webpages. Guo et al 28 proposed a phishing webpages detection approach which they called HinPhish. The approach establishes a heterogeneous information network (HIN) based on domain nodes and loading resources nodes and establishes three relationships between the four hyperlinks: external link, empty link, internal link and relative link.…”

Section: Related Workmentioning

confidence: 99%

An effective detection approach for phishing websites using URL and HTML features

Aljofey

Jiang

Rasool

et al. 2022

Sci Rep

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Today's growing phishing websites pose significant threats due to their extremely undetectable risk. They anticipate internet users to mistake them as genuine ones in order to reveal user information and privacy, such as login ids, pass-words, credit card numbers, etc. without notice. This paper proposes a new approach to solve the anti-phishing problem. The new features of this approach can be represented by URL character sequence without phishing prior knowledge, various hyperlink information, and textual content of the webpage, which are combined and fed to train the XGBoost classifier. One of the major contributions of this paper is the selection of different new features, which are capable enough to detect 0-h attacks, and these features do not depend on any third-party services. In particular, we extract character level Term Frequency-Inverse Document Frequency (TF-IDF) features from noisy parts of HTML and plaintext of the given webpage. Moreover, our proposed hyperlink features determine the relationship between the content and the URL of a webpage. Due to the absence of publicly available large phishing data sets, we needed to create our own data set with 60,252 webpages to validate the proposed solution. This data contains 32,972 benign webpages and 27,280 phishing webpages. For evaluations, the performance of each category of the proposed feature set is evaluated, and various classification algorithms are employed. From the empirical results, it was observed that the proposed individual features are valuable for phishing detection. However, the integration of all the features improves the detection of phishing sites with significant accuracy. The proposed approach achieved an accuracy of 96.76% with only 1.39% false-positive rate on our dataset, and an accuracy of 98.48% with 2.09% false-positive rate on benchmark dataset, which outperforms the existing baseline approaches.

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KGhish: A Phishing Website Detection Method Based on Knowledge Graph

Liu,

Wang,

Chen

et al. 2024

Lecture Notes in Computer Science

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HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks

Cited by 7 publications

References 41 publications

Phishing uniform resource locator detection using machine learning: A step towards secure system

Phishing uniform resource locator detection using machine learning: A step towards secure system

An effective detection approach for phishing websites using URL and HTML features

KGhish: A Phishing Website Detection Method Based on Knowledge Graph

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