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Student Project | Computer Engineering | India | Volume 12 Issue 10, October 2023 | Popularity: 4.6 / 10
Phishing Website Detection using Machine Learning Rules with Cryptography Technique
S. Thamizhazhaki, J. Pragathi
Abstract: Malicious URLs and websites pose a frequent and serious risk to online safety. Search engines naturally become the foundation of information management. However, the proliferation of fraudulent websites on search engines has put our users at serious risk. Most current techniques for detecting rogue websites concentrate on a particular assault. At the same time, numerous websites are unaffected by the readily available browser extensions based on blacklists. Since the server cannot deduce any useful information from the masked data, it is crucial that any data leaving the client side be effectively masked. The initial PPSB service is here suggested. Strong security guarantees are provided, something that is lacking in the current SB services. The proposed approach detects the malicious URL access with the help of blacklist storage. The input URL (given by user) was classified with the help of SVM classification. SVM is a type of machine learning algorithm that accurately detect, whether the URL is safe or unsafe. In particular, it carries over the capacity to recognize harmful URLs while safeguarding the browsing history and proprietary information of the blacklist provider (the list of unsafe URLs) as well as the user's privacy. In order to protect user privacy from outside analysts and service providers, a model that encrypts sensitive data was presented in this work. Additionally fully supports selective aggregate functions for analysing online user behaviour and ensuring differential privacy. Data about users' online behaviour is encrypted using the Homomorphic RSA technique.
Keywords: Malicious URL Detection, Blacklist Creation, History encryption using Homomorphic RSA, URL Recommendation, Key verification, History Access
Edition: Volume 12 Issue 10, October 2023
Pages: 644 - 650
DOI: https://www.doi.org/10.21275/MR231005145043
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