Rising phishing attacks pose serious cybersecurity threats due to their use of fraudulent links to collect confidential user information. In this paper, we evaluate the performance of various Machine Learning (ML) models, including Decision Trees, Random Forest, and Extreme Gradient Boosting, to address this growing threat. Additionally, we assess the effectiveness of different feature selection techniques, such as Analysis of Variance, Correlation-based Selection, Mutual Information, and Recursive Feature Elimination with Cross-Validation. Our findings demonstrate that combining Extreme Gradient Boosting with Recursive Feature Elimination and Cross-Validation outperforms previous methods. The proposed solution achieved an accuracy of 97.33%, a recall of 97.1656%, an F1 score of 97.3%, and a precision of 97.42%, highlighting its potential for effectively identifying phishing attacks

In an ever-evolving digital landscape, the significance of robust cybersecurity measures continues to grow. This paper explores the efficacy of fortifying organizational defenses through the adoption of open-source Security Information and Event Management (SIEM) solutions. Through a comprehensive analysis, it is revealed that Wazuh emerges as the optimal choice due to its notable attributes including reliability, cost-effectiveness, high endpoint availability, robust file monitoring capabilities, intrusion detection prowess, and scalability. These findings offer valuable insights for organizations seeking to bolster their cybersecurity infrastructure, empowering them to make informed decisions regarding the integration of Wazuh into their security frameworks.