Web Mining Techniques to Block Spam Web Sites

EL-Mohdy, Esraa M.; Elgamal, A. F.; Abdelkader, Hanan E.

doi:10.5120/ijca2018917622

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Another research focused on web spam detection was conducted by Al-Kabi et al [148] proposed an integrated online Arabic web spam detection system (OLAWSDS) that filters malicious pages from search engines. They extracted 18 lexical and content features.…”

Section: ) Lexical and 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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Section: ) Lexical and Content-based Features Studiesmentioning

confidence: 99%