• Dr. R. S. Khule
• Shraddha Jadhav
• Nikita Sanap
• Shital Satote
• Sakshi Ugale

Phishing attacks pose a severe threat to online security, making the accurate detection of phishing URLs a critical endeavor. This abstract introduces a novel approach that leverages intelligent methods based on machine learning algorithms to enhance the precision of phishing URL detection. In an era where cybercriminals continuously adapt and evolve their tactics, this research aims to develop a robust and proactive defense mechanism. By harnessing the power of machine learning, this study seeks to train models capable of identifying subtle patterns and characteristics in URLs that are indicative of phishing attempts. Through a comprehensive analysis of various features, including domain structure, textual content, and lexical attributes, the proposed methods aim to provide a multi-faceted and dynamic approach to detect phishing URLs, ultimately bolstering cybersecurity efforts. The proposed work focuses on the design and evaluation of a range of machine learning algorithms, including decision trees, random forests, and naive bays, to uncover the most effective techniques for phishing URL detection. By harnessing a diverse set of features and utilizing labeled datasets for training, the models are expected to learn and adapt to emerging phishing techniques. As phishing threats continue to evolve in complexity and scale, the integration of intelligent methods based on machine learning is poised to be a critical step forward in the ongoing battle to protect digital identities and sensitive information.

Phishing URL Detection, Cybersecurity, Feature Analysis, Decision Trees, Random Forests, Pattern Recognition

"INTELLIGENT METHODS FOR ACCURATELY DETECTING PHISHING WEBSITES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.d537-d540, November-2023, Available :http://www.jetir.org/papers/JETIR2311367.pdf

"INTELLIGENT METHODS FOR ACCURATELY DETECTING PHISHING WEBSITES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppd537-d540, November-2023, Available at : http://www.jetir.org/papers/JETIR2311367.pdf