  Cybersecurity is one of the global issues because of the extensive dependence
on cyber systems of individuals, industries, and organizations. Among the cyber
attacks, phishing is increasing tremendously and affecting the global economy.
Therefore, this phenomenon highlights the vital need for enhancing user
awareness and robust support at both individual and organizational levels.
Phishing URL identification is the best way to address the problem. Various
machine learning and deep learning methods have been proposed to automate the
detection of phishing URLs. However, these approaches often need more
convincing accuracy and rely on datasets consisting of limited samples.
Furthermore, these black box intelligent models decision to detect suspicious
URLs needs proper explanation to understand the features affecting the output.
To address the issues, we propose a 1D Convolutional Neural Network (CNN) and
trained the model with extensive features and a substantial amount of data. The
proposed model outperforms existing works by attaining an accuracy of 99.85%.
Additionally, our explainability analysis highlights certain features that
significantly contribute to identifying the phishing URL.
