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@article{164321,
        author = {Tummala Revanth Mahindra and B. Swetha and Kappara Sri Sai Samanvita and Nalla Puneeth Krishna Reddy},
        title = {Phishing website detection through website traffic using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1339-1334},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164321},
        abstract = {Web sites traffic largely encourage the expansion of illegal activities on the Internet and restrict the growth of Web services. Consequently, there's been a great drive for the development of methodical approaches to discourage consumers visiting these kinds of websites. Our suggestion is to use a learning-based method to divide websites into two categories: high and low. Our approach does not access the content of the websites; it just analyzes the Uniform Resource Locator. Consequently, it removes the chance of exposing users to browser-based vulnerabilities and run-time latency. With the help of learning algorithms, our system performs better in terms of generality and coverage with the blacklist service.
The website URLs are divided into two classes: 
High: Secure websites offering standard services
Low: Websites try to overwhelm consumers via advertisements or other content, such deceptive surveys.
},
        keywords = {Machine Learning Classification, Phishing Detection, Random Forest Algorithm.},
        month = {},
        }