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@article{159906,
        author = {PRASANNA KUMAR M and Dhanraj and Bhavanishankar K},
        title = {MALICIOUS URL DETECTION USING MACHINE LEARNING AND DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {12},
        pages = {768-774},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159906},
        abstract = {The quantity and magnitude of network information security risks have continually rising. Hackers today mostly employ techniques that target technology from beginning to conclusion and take advantage of human weakness. These methods include pharming, phishing, and social engineering, among others. These attacks include a number of phases, one of which is to trick users through malicious Uniform Resource Locators (URLs). In light of this, malicious URL detecting is a hot subject right now. A variety of academic research have demonstrated several ways to identify malicious URLs using machine learning and deep learning technologies. Based on our hypothesized URL behaviours and characteristics, we provide a machine learning-based solution for detecting malicious URLs in this work. Furthermore, big data technology is applied to enhance the ability to appreciate fraudulent URLs based on aberrant activity. A novel collection of URL traits and behaviours, a machine learning algorithm, and big data technologies make up the suggested detection method, to summarize it. The experimental findings indicate that the specified URL features and behaviour can increase total the capacity to identify dangerous URLs. This suggests that the proposed methodology may be seen as a successful and consumer method of identifying dangerous URLs.},
        keywords = {phishing, machine learning, malicious URL detection},
        month = {},
        }