WEB SPOOFING DEFENSE EMPOWERING USERS WITH PHISHCATCHER'S MACHINE LEARNING - Gowsic K; Shreenithi S; Sri Samyuktha M; Swathi K (ShodhKosh: Journal of Visual and Performing Arts, 2024)
The threat of malicious URLs and websites poses a frequent and serious risk to online safety. Search engines naturally serve as the foundation of information management. However, the proliferation of fake websites on these platforms puts our users in grave danger. Many current methods for identifying rogue websites focus on specific attacks, leaving numerous websites unaffected by widely available blacklist-based browser updates. It's imperative to properly disguise any data leaving the client side, as the server cannot extract meaningful information from masked data. This paper proposes an initial Privacy-Preserving Secure Browsing (PPSB) service, offering robust security assurances lacking in existing Secure Browsing (SB) services. The suggested method utilizes blacklist storage to detect malicious URL access, employing SVM classification to analyze user-provided input URLs. SVM, a class of machine learning algorithm, reliably assesses the safety or riskiness of a URL while safeguarding user privacy, browsing history, and the proprietary information of the blacklist provider. The paper introduces a technique for encrypting critical data to protect user privacy from external analysts and service providers, while fully supporting selected aggregate functionalities for analyzing user online activities and ensuring differential privacy. The ABE Encryption method encrypts user behavior data, enhancing secure history access.