Enhancing Phishing Detection in Cloud Environments Using RNN-LSTM in a Deep Learning Framework

Abstract

Phishing attacks targeting cloud computing services are more sophisticated and require advanced detection mechanisms to address evolving threats. This study introduces a deep learning approach leveraging recurrent neural networks (RNNs) with long short-term memory (LSTM) to enhance phishing detection. The architecture is designed to capture sequential and temporal patterns in cloud interactions, enabling precise identification of phishing attempts. The model was trained and validated using a dataset of 10,000 samples, adapted from the PhishTank repository. This dataset includes a diverse range of attack vectors and legitimate activities, ensuring comprehensive coverage and adaptability to real-world scenarios. The key contribution of this work includes the development of a high-performance RNN-LSTM-based detection mechanism optimized for cloud-specific phishing patterns that achieve 98.88% accuracy. Additionally, the model incorporates a robust evaluation framework to assess its applicability in dynamic cloud environments. The experimental results demonstrate the effectiveness of the proposed approach, surpassing existing methods in accuracy and adaptability.