Abstract
In recent years cyberattacks have become destructive and targeted. With technological advancements, diverse threats are launching in a sophisticated way that targets people to defraud them. Many web applications have been struggling to improve the reliability and security of their platforms to protect users from fraud, revenue, or malware. These attacks use malicious uniform resource locators (URLs) to attack web users. These URLs host unwanted content in the form of junk emails, phishing, or unauthorized drive-by downloads. Unsuspecting people click these phishing URLs and become victims of unethical anonymous activities like identity theft (personal or financial details) and installation of viruses. Therefore, it is necessary to detect malicious URLs accurately for resolving security issues. Traditional protection method, such as blacklisting, remains a classical technique for the detection of malicious URLs due to its simplicity but cannot detect unknown malicious URLs; hence, machine learning approaches are being used for achieving better results. This chapter aims to provide a structural understanding of popular feature extraction techniques and machine learning algorithms.
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Swarnkar, M., Sharma, N., Kumar Thakkar, H. (2023). Malicious URL Detection Using Machine Learning. In: Thakkar, H.K., Swarnkar, M., Bhadoria, R.S. (eds) Predictive Data Security using AI. Studies in Computational Intelligence, vol 1065. Springer, Singapore. https://doi.org/10.1007/978-981-19-6290-5_11
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