International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 4
July-August 2025

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CYBER DECEPTION DETECTOR THROUGH MACHINE LEARNING

Author(s) ILAMATHY R, SUBHA K
Country India
Abstract Attackers and other cybercriminals are making the internet hazardous as the majority of human activities shift online by posing a severe risk to customers and businesses, endangering global security, and undermining the economy. Nowadays, phishes are always coming up with fresh ways to trick users into disclosing their private data. It is crucial to build phishing detection algorithms in order to prevent falling prey to online crooks. For phishing detection, machine learning or data mining techniques are utilised, such as classification that divides online users into dangerous or safe users, or regression that forecasts the likelihood of being attacked by some online criminals in a specific time frame. In the past, a number of solutions for phishing detection have been put out, but the search for a better solution is still ongoing due to the dynamic nature of some of the numerous phishing schemes used by cybercriminals. This project aims to classify phishing websites using a machine learning framework. Techniques such as the Random Forest algorithm will be utilized for accurate detection and classification of phishing sites. Applied using benchmark datasets that are gathered from KAGGLE websites, experimental findings demonstrate that the suggested method offers better accuracy rate compared to the current techniques.
Keywords KAGGLE Random Forest algorithm.
Field Engineering
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-23
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.51715
Short DOI https://doi.org/g9t2dq
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E-ISSN 2582-2160
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