A Systematic Review on Deep-Learning-Based Phishing Email Detection

Abstract

Phishing attacks are a growing concern for individuals and organizations alike, with the potential to cause significant financial and reputational damage. Traditional methods for detecting phishing attacks, such as blacklists and signature-based techniques, have limitations that have led to developing more advanced techniques. In recent years, machine learning and deep learning techniques have gained attention for their potential to improve the accuracy of phishing detection. Deep learning algorithms, such as CNNs and LSTMs, are designed to learn from patterns and identify anomalies in data, making them more effective in detecting sophisticated phishing attempts. To develop a comprehensive understanding of the current state of research on the use of deep learning techniques for phishing detection, a systematic literature review is necessary. This review aims to identify the various deep learning techniques used for phishing detection, their effectiveness, and areas for future research. By synthesizing the findings of relevant studies, this review identifies the strengths and limitations of different approaches and provides insights into the challenges that need to be addressed to improve the accuracy and effectiveness of phishing detection. This review aims to contribute to developing a coherent and evidence-based understanding of the use of deep learning techniques for phishing detection. The review identifies gaps in the literature and informs the development of future research questions and areas of focus. With the increasing sophistication of phishing attacks, applying deep learning in this area is a critical and rapidly evolving field. This systematic literature review aims to provide insights into the current state of research and identify areas for future research to advance the field of phishing detection using deep learning.

Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models

“…This stems from the potential of deep learning to overcome the limitations of traditional methods and enhance the accuracy of detection. CNN, LSTM, and GRU architectures are capable of analyzing the contents and structure of phishing emails, as demonstrated in various studies [3]. However, a major success measure for any phishing detection model is the ability to detect zero-day phishing attacks with low false-positive rates.…”

“…In the literature, classical approaches for phishing detection fall into two categories: blacklists and signature-based techniques [3]. Blacklisting is the act of making a list of suspicious resources used in previous phishing attacks.…”