Website categorization via design attribute learning

Cohen, Doron; Naim, Or; Toch, Eran; Ben‐Gal, Irad

doi:10.1016/j.cose.2021.102312

Cited by 8 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper extended the study of Cohen et al [24] by developing an algorithm that automatically extracts websites' features, including full screenshots and image analysis capabilities, in a large-scale operation and enriches each website record with third-party data regarding its operation and metadata. Advanced machine learning (ML) classi cation models were then applied to determine whether each website is malicious.…”

Section: Experimental Settingsmentioning

confidence: 94%

“…In terms of design attributes, we refer to all visual and nonvisual elements that a web page consists of [24]. Among these attributes, we can nd HTML code and hierarchies, JavaScript, CSS, color tables, styles, font types, objects, etc.…”

Section: Methodsmentioning

confidence: 99%

“…Cohen et al [24] proposed a website assessment scheme based on the website design features (visual and nonvisual features) contained on a web page and their related features. The algorithm implemented by Coen et al utilized the web page URL, HTML, DOM and CSS for website classi cation and was able to achieve a classi cation accuracy of over 90%.…”

Section: Related Workmentioning

confidence: 99%

“…This fundamental con ict can be exploited to create a robust and sustainable classi cation approach. As suggested in a previous work by Cohen et al [24] websites can be accurately classi ed and categorized by their design attributes.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Malicious website identification using design attribute learning

Naim

Cohen

Ben‐Gal

2023

Int. J. Inf. Secur.

Self Cite

View full text Add to dashboard Cite

Malicious websites pose a challenging cybersecurity threat. Traditional tools for detecting malicious websites rely heavily on industry-speci c domain knowledge, are maintained by large-scale research operations, and result in a never-ending attacker-defender dynamic. Malicious websites need to balance two opposing requirements to successfully function: escaping malware detection tools while attracting visitors. This fundamental con ict can be leveraged to create a robust and sustainable detection approach based on the extraction, analysis and learning of design attributes for malicious website identi cation.In this paper, we propose a next-generation algorithm for extended design attribute learning that learns and analyzes web page structures, contents, appearances and reputations to detect malicious websites. A large-scale experiment that was conducted on more than 35,000 websites suggests that the proposed algorithm effectively detects more than 83% of all malicious websites while maintaining a low falsepositive rate of 2%. In addition, the proposed method can incorporate user feedback and ag new suspicious websites and thus can be effective against zero-day attacks.

show abstract

Section: Experimental Settingsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Malicious website identification using design attribute learning

Naim

Cohen

Ben‐Gal

2023

Int. J. Inf. Secur.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Content categorization falls into use cases of machine learning or probabilistic models as they can be trained to automatically categorize or classify content into predefined categories based on the content's characteristics, features, or patterns. Techniques needed for achieving a success in these fields have been developed by researchers generally in purpose of cracks and malicious websites detection [2], web navigation prediction [5], fake news detection [1], Search Engine Optimization [11]. All the purposes of websites or generally content classification utilized different methods of data acquisition and preprocessing, feature extraction and machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Review of Modelling Approaches for Website-Related Predictions

Mauer

2024

IAPGOS

View full text Add to dashboard Cite

This paper researches various modelling approaches for website-related predictions, offering an overview of the field. With the ever-expanding landscape of the World Wide Web, there is an increasing need for automated methods to categorize websites. This study examines an array of prediction tasks, including website categorization, web navigation prediction, malicious website detection, fake news website detection, phishing website detection, and evaluation of website aesthetics.

show abstract

Impacts of Website Design Attributes and Functional Benefits on Consumer-Instrumental Value in Electronic Commerce Apparel Websites

Krithika,

Vasantha

2024

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Website categorization via design attribute learning

Cited by 8 publications

References 35 publications

Malicious website identification using design attribute learning

Malicious website identification using design attribute learning

Review of Modelling Approaches for Website-Related Predictions

Impacts of Website Design Attributes and Functional Benefits on Consumer-Instrumental Value in Electronic Commerce Apparel Websites

Contact Info

Product

Resources

About