PhishNull: Enhancing Cyber Hygiene Through Supervised Machine Learning

Author(s)	Purva Kulkarni, Siddhi Jadhav, Tanya Gupta, Sangeeta Mishra
Country	India
Abstract	The research paper addresses the pervasive issue of phishing within the realm of Internet security, acknowledging its persistence despite the advancements in antivirus and technical safeguards. Focusing on combating this online scam, the study delves into two primary methodologies: Black Listing and Machine Learning. Opting for a Machine Learning and heuristic-based approach, the thesis conducts a comparative analysis of various Machine Learning algorithms, including Logistic Regression, alongside ensemble algorithms such as Adaboost and Gradient Boost. While initial expectations leaned towards ensemble algorithms yielding superior results, the outcomes revealed a nuanced reality. Although ensemble algorithms demonstrated promising predictive capabilities, their performance did not surpass expectations.
Keywords	Phishing, Internet security, Machine Learning, Black Listing, Heuristic-based approach, Logistic Regression, Ensemble algorithms, Adaboost, Gradient Boost, Comparative analysis
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-04-27
Cite This	PhishNull: Enhancing Cyber Hygiene Through Supervised Machine Learning - Purva Kulkarni, Siddhi Jadhav, Tanya Gupta, Sangeeta Mishra - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18546
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.18546
Short DOI	https://doi.org/gtsg4x

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