Phishing Attack Detection Using Natural Language Processing and Deep Learning

Phishing attacks are one of the most prevalent forms of cybercrime, exploiting human vulnerabilities through deceptive tactics to steal sensitive information, such as passwords, credit card details, and social security numbers. These attacks often involve fraudulent emails, websites, and other online communication channels that appear to be from trusted sources. Traditional methods for detecting phishing attacks, such as signature-based approaches, are becoming increasingly ineffective as cybercriminals evolve their strategies. In recent years, the application of Natural Language Processing (NLP) and Deep Learning (DL) has emerged as a promising solution for enhancing phishing attack detection systems. NLP techniques can analyze textual data from phishing emails, messages, and websites, identifying suspicious patterns that may be indicative of phishing. Deep learning models, particularly neural networks, can process and classify these patterns with high accuracy. This paper explores the use of NLP and deep learning for phishing attack detection, providing an overview of existing research and advancements in this area. Key NLP techniques such as text classification, entity recognition, and sentiment analysis are examined in the context of phishing detection. Additionally, the paper reviews various deep learning architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), that have been used to automatically identify phishing content in emails and websites. Furthermore, the paper presents case studies and performance metrics of phishing detection systems, demonstrating how NLP and DL models outperform traditional methods. The future direction of this research includes enhancing model interpretability, reducing false positives, and improving generalization across different languages and attack vectors.