Malicious URLs has become a significant threat in the field of cybersecurity nowadays. Prediction of malicious URLs among many URLs is a critical challenge in the area of cybersecurity. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large number of URLs. Many ML techniques are used in recent days in different areas of the Internet of Things (IoT). Various studies have been proposed with ML techniques to detect the malicious URLs. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of malicious URLs. The prediction model is dealt with different combinations of training methods and several known classification techniques. We produce an efficient performance level with an accuracy level of 99.7% through the prediction model for detecting malicious URLs with the support vector machine.
Software And Hardware