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- W4386827208 abstract "The existing research concentrated on the binary classification of whether the website is malicious or benign; but a few researchers adopted a multiclass classification framework to filter out the attacks from compromised URLs. The intruders spread the attacks through a malicious URL and upon clicking download the viruses and redirect the user to malicious websites. This chapter proposes a machine learning (ML) approach based on URL lexical features, to identify multiple cyber-attacks like phishing, malware, spam, and defacement attempted through URLs. The authors have created their own dataset, consisting of 29 lexical features of the URL and the model is trained on the basis of these features. Different ML classifiers were used for evaluating the URL features and generating an optimal base model. The 10-fold cross-validation method is adopted to train and test each observation. The experiments show that bagging implementation of a decision tree classifier achieves an accuracy of 94.1% in detecting different attacks from the malicious URL by simply considering the lexical feature of the URL." @default.
- W4386827208 created "2023-09-19" @default.
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- W4386827208 date "2024-01-05" @default.
- W4386827208 modified "2023-09-27" @default.
- W4386827208 title "Detection of Cyber Threats From Suspicious URLs Using Multi-Classification Approach" @default.
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- W4386827208 doi "https://doi.org/10.4018/979-8-3693-1186-8.ch007" @default.
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