Malicious Url Detection Pdf Malware Phishing We examined and summarized related work in the detection of malicious urls on arabic and english websites using ml algorithms. This paper presents a comprehensive review of malicious url detection technologies, systematically analyzing methods from traditional blacklisting to advanced deep learning approaches (e.g., transformer, gnns, and llms).
Malicious Url Detection Based On Machine Learning Download Free Pdf Both detection techniques rely on url characteristics such as length, number of vowels and others to classify them as legitimate or malicious. the main contribution of this paper is to propose a taxonomy of detection techniques and to point out which url characteristics are used by each method. Treating the problem as a multi class classification challenge, raw urls are categorized into different types, including benign or safe urls, phishing urls, malware urls, and defacement urls using various machine learning algorithms. 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. This paper presents a comprehensive review of malicious url detection technologies, systematically analyzing methods from traditional blacklisting to advanced deep learning ap proaches (e.g. transformer, gnns, and llms).
Malicious Url Detection Based On Machine Learning Abstract Pdf 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. This paper presents a comprehensive review of malicious url detection technologies, systematically analyzing methods from traditional blacklisting to advanced deep learning ap proaches (e.g. transformer, gnns, and llms). After categorizing detection methods based on feature modality and reviewing their application in specific contexts such as arabic web environments, it is also crucial to move beyond taxonomy and assess the real world performance and inherent limitations of these approaches. Based on the aspects such as language, url features, ml techniques and datasets used, we present a categorisation of the studies reviewed for detection of malicious urls(aljabri, altamimi, et al., 2022a). 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. 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.
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