A Comprehensive Evaluation of Webpage Content Features for Detecting Malicious Websites

McGahagan, John; Bhansali, Darshan; Pinto-Coelho, Ciro; Cukier, Michel

doi:10.1109/ladc48089.2019.8995713

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…However, the overall accuracy was only 90%, and the FPR was 8%. McGahagan performed a comprehensive evaluation of web page content for detecting malicious websites via 8 different supervised machine learning models and reported an accuracy of 89% with an FPR that could reach 10% [44]. This work emphasized both the potential and the challenge of using static analysis for detection of nonphishing malicious websites and raised concerns regarding the ability to implement this approach in a real-life commercial scenario due to the high induced FPR.…”

Section: Related Workmentioning

confidence: 99%

Malicious website identification using design attribute learning

Naim

Cohen

Ben‐Gal

2023

Int. J. Inf. Secur.

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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.

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Section: Related Workmentioning

confidence: 99%

Malicious website identification using design attribute learning

Naim

Cohen

Ben‐Gal

2023

Int. J. Inf. Secur.

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“…However, its biggest issue is that it is useless when dealing with compromised websites and servers [5]. For this reason, researchers have suggested using content-based features (HTML, CSS, JavaScript, images, text, …) instead of URLs [17].…”

Section: Url-based Approachesmentioning

confidence: 99%

Multi-Modal Deep Learning for Effective Malicious Webpage Detection

Belfedhal

2023

RIA

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The pervasive threat of malicious webpages, which can lead to financial loss, data breaches, and malware infections, underscores the need for effective detection methods. Conventional techniques for detecting malicious web content primarily rely on URL-based features or features extracted from various webpage components, employing a single feature vector input into a machine learning model for classifying webpages as benign or malicious. However, these approaches insufficiently address the complexities inherent in malicious webpages. To overcome this limitation, a novel Multi-Modal Deep Learning method for malicious webpage detection is proposed in this study. Three types of automatically extracted features, specifically those derived from the URL, the JavaScript code, and the webpage text, are leveraged. Each feature type is processed by a distinct deep learning model, facilitating a comprehensive analysis of the webpage. The proposed method demonstrates a high degree of effectiveness, achieving an accuracy rate of 97.90% and a false negative rate of a mere 2%. The results highlight the advantages of utilizing multi-modal features and deep learning techniques for detecting malicious webpages. By considering various aspects of web content, the proposed method offers improved accuracy and a more comprehensive understanding of malicious activities, thereby enhancing web user security and effectively mitigating the risks associated with malicious webpages.

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“…Altay et al [140] proposed classifying web pages using supervised ML techniques and a dataset collected from PhishTank [14] and Alexa [16]. The data were extracted from web pages using a keyword density extractor library designed by Comodo Group [141], and 8,000 content features were extracted. The achieved accuracy of 98.24% with SVM-Radial basis function (SVM-RBF).…”

Section: ) Content-based Features Studiesmentioning

confidence: 99%

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

et al. 2022

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In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers' inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user's system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis that we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

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A Comprehensive Evaluation of Webpage Content Features for Detecting Malicious Websites

Cited by 13 publications

References 29 publications

Malicious website identification using design attribute learning

Malicious website identification using design attribute learning

Multi-Modal Deep Learning for Effective Malicious Webpage Detection

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

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