Enhancing Arabic Phishing Email Detection: A Hybrid Machine Learning Based on Genetic Algorithm Feature Selection

Recently owing to widespread Internet use and technological breakthroughs, cyber-attacks have increased. One of the most common types of attacks is phishing, which is executed through email and is a leading cause of the recent surge in cyber-attacks. These attacks maliciously demand sensitive or private information from individuals and companies. Various methods have been employed to address this issue by classifying emails, such as feature-based classification and manual verification. However, these methods face significant challenges regarding computational efficiency and classification precision. This work presents a novel hybrid approach that combines machine learning and deep learning techniques to improve the identification of phishing emails containing Arabic content. A genetic algorithm is employed to optimize feature selection, thereby enhancing the performance of the model by effectively identifying the most relevant features. The novel dataset comprises 1,173 records categorized into two classes: phishing and legitimate. A number of empirical investigations were carried out to assess and contrast the performance outcomes of the proposed model. The findings reveal that the proposed hybrid model outperforms other machine learning classifiers and standalone deep learning models.