Social Engineering Detection : Phishing URLs


Authors : Utkarsh Singh; Ashvini Kumar; Pratham Jain; Tanya Jaiswal; Sudhanshu Shekhar; Gurleen Kaur

Volume/Issue : Volume 8 - 2023, Issue 10 - October

Google Scholar : https://tinyurl.com/4d9mr5b7

Scribd : https://tinyurl.com/mtmaay96

DOI : https://doi.org/10.5281/zenodo.10082434

Abstract : In the digital age, the proliferation of malicious phishing URLs poses a significant threat to online security. While conventional machine learning algorithms have been employed to combat this menace, our research pioneers the use of ensemble methods, including XGBoost and Random Forest, for phishing URL detection. Our methodology involves collection of the data, preprocessing it then feature extraction followed by model training, evaluation and comparison. Notably, our results reveal the superior accuracy of ensemble methods in distinguishing phishing URLs from legitimate ones. These findings underscore the potential of ensemble methods as a game-changing asset in the battle against cyber threats, promising enhanced online security and the protection of sensitive user information.

Keywords : Social Engineering, Phishing URLs, Cyber Security, Machine Learning.

In the digital age, the proliferation of malicious phishing URLs poses a significant threat to online security. While conventional machine learning algorithms have been employed to combat this menace, our research pioneers the use of ensemble methods, including XGBoost and Random Forest, for phishing URL detection. Our methodology involves collection of the data, preprocessing it then feature extraction followed by model training, evaluation and comparison. Notably, our results reveal the superior accuracy of ensemble methods in distinguishing phishing URLs from legitimate ones. These findings underscore the potential of ensemble methods as a game-changing asset in the battle against cyber threats, promising enhanced online security and the protection of sensitive user information.

Keywords : Social Engineering, Phishing URLs, Cyber Security, Machine Learning.

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