Intelligent Visual Similarity-Based Phishing Websites Detection

Chen, Jiann-Liang; Ma, Yi-Wei; Huang, Kuan-Lung

doi:10.3390/sym12101681

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…. 18. Initial experiments showed that the best results were obtained with the 80/20 split, which was used for the remaining combinations of features (m = 6. .…”

Section: Methodsmentioning

confidence: 99%

“…The machine learning approach is most common for the analysis of website content and address. Content analysis is based on the features related to HTML (Hyper Text Markup Language) [14][15][16], body text [17], images [17][18][19][20], domain registration information [15,21] and styles [22]. The URL analysis [21,[23][24][25][26][27] is usually based on the features related to the length of the full URL, the domain name, the directory, the types of symbols, the protocol, the number of symbols, etc.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Evaluation of Possible Feature Combinations for the Detection of Fraudulent Online Shops

Janavičiūtė,

Liutkevičius,

Dabužinskas

et al. 2024

Applied Sciences

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Online shopping has become a common and popular form of shopping, so online attackers try to extract money from customers by creating online shops whose purpose is to compel the buyer to disclose credit card details or to pay money for goods that are never delivered. Existing buyer protection methods are based on the analysis of the content of the online shop, customer reviews, the URL (Uniform Resource Locator) of the website, the search in blacklists or whitelists, or the combination of the above-mentioned methods. This study aims to find the minimal set of publicly and easily obtainable features to create high-precision classification solutions that require little computing and memory resources. We evaluate various combinations of 18 features that belong to three possible categories, namely URL-based, content-based, and third-party services-based. For this purpose, the custom dataset is created, and several machine learning models are applied for the detection of fraudulent online shops based on these combinations of features. The results of this study show that even only four of the most significant features allow one to achieve 0.9342 classification accuracy, while 0.9605 accuracy is reached with seven features, and the best accuracy of 0.9693 is achieved using thirteen and fifteen features.

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“…. 18. Initial experiments showed that the best results were obtained with the 80/20 split, which was used for the remaining combinations of features (m = 6. .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Possible Feature Combinations for the Detection of Fraudulent Online Shops

Janavičiūtė,

Liutkevičius,

Dabužinskas

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Page similarity has been extensively investigated in the literature to detect phishing web pages (see, e.g., [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58]). In fact, as already pointed out, web pages are made in a way to look very similar or identical to their legitimate counterparts, thus their similarity is a good indicator of a phishing attack.…”

Section: Page Similarity-based Detection Methodsmentioning

confidence: 99%

Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

2023

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Phishing is a security threat with serious effects on individuals as well as on the targeted brands. Although this threat has been around for quite a long time, it is still very active and successful. In fact, the tactics used by attackers have been evolving continuously in the years to make the attacks more convincing and effective. In this context, phishing detection is of primary importance. The literature offers many diverse solutions that cope with this issue and in particular with the detection of phishing websites. This paper provides a broad and comprehensive review of the state of the art in this field by discussing the main challenges and findings. More specifically, the discussion is centered around three important categories of detection approaches, namely, list-based, similarity-based and machine learning-based. For each category we describe the detection methods proposed in the literature together with the datasets considered for their assessment and we discuss some research gaps that need to be filled.

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“…Chen et al 6 proposed a method to detect phishing websites. The websites are first classified based on the level of similarity and separate methods are applied to each category.…”

Section: Literature Reviewmentioning

confidence: 99%

Visual similarity-based phishing detection using deep learning

Saeed

2022

J. Electron. Imag.

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Phishing is a common cybercrime that tempts a target to reveal personal and financial information by imitating a legitimate webpage. Classical techniques, such as list-based, and heuristic approaches have focused on identifying the uniform resource locator (URL). However, cybercriminals have uncovered several methods to evade URL-based techniques. Recently, researchers have started developing techniques that are based on visual similarity to detect phishing webpages. However, using visual similarity raises several computer vision-related issues such as webpage segmentation and feature extraction. Deep learning provides a suitable solution to these issues as it integrates feature extraction, feature selection, and classification in an end-toend system. We have made the first attempt to detect phishing webpages based on visual similarity by modifying and retraining deep neural networks. A comprehensive evaluation on two publicly available databases has shown a marked improvement of around 7% classification accuracy.

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Intelligent Visual Similarity-Based Phishing Websites Detection

Cited by 19 publications

References 15 publications

Experimental Evaluation of Possible Feature Combinations for the Detection of Fraudulent Online Shops

Experimental Evaluation of Possible Feature Combinations for the Detection of Fraudulent Online Shops

Phishing or Not Phishing? A Survey on the Detection of Phishing Websites

Visual similarity-based phishing detection using deep learning

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