Abstract: Phishing attacks pose a significant threat to cybersecurity, necessitating effective detection mechanisms. This study explores the application of machine learning algorithms for the automated identification of phishing websites. By collecting a dataset of URLs labelled as phishing or legitimate, relevant features are extracted, pre-processed, and used to train various machine learning models. The performance of these models is evaluated using metrics such as accuracy, precision, recall, and F1-score, highlighting their effectiveness in distinguishing between phishing and legitimate URLs. Continuous monitoring and updates are emphasized to adapt to evolving phishing tactics. This research provides practical insights into the application of machine learning for phishing detection, contributing to the advancement of cybersecurity measures.
Keywords: Phishing detection, Machine learning, Cybersecurity, Feature extraction, Model evaluation
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DOI:
10.17148/IJARCCE.2024.134145
