Phishing URL Detection Using Machine Learning and Deep Learning in NLP

Phishing attacks are an emerging cyber threat that makes use of fake URLs to pilfer sensitive information of users, such as their login credentials and other financial details. In line with this, the present study involves the adoption of NLP methods with machine-learning algorithms in effectively detecting phishing URLs. To this end, five different algorithms are considered for effectiveness in the study: LSTM, GRU, Random Forest, Support Vector Machine, and Logistic Regression. Each of them was trained and then tested with a complete dataset, containing phishing and legitimate URLs for correctness in distinguishing malicious URLs from the benign ones. Amongst them, the LSTM model recorded an accuracy as high as 98% due to its capability for handling sequential dependencies in addition to contextual patterns within the URLs. This research underlines the promise of LSTM-based models in fighting phishing threats and further proposes a scalable solution to enable real-time phishing URL detection and enhance cybersecurity defense.