Malicious URL Detection using Multilayer CNN

Due to developing Internet-based technologies, the number of online domains and URLs is increasing globally. Parallel several cybersecurity threats and phishing attacks have been encountered while accessing these websites. Accessing a malicious webpage can create serious harm to the physical system. Data loss, privacy breach, credential theft and many security threats are entered while an Internet user clicks a malicious URL. Several defence and detection strategies have been proposed in the previous research works. But, the works have used a traditional classifier which is not adequate. This is because the size of the URL is huge, and the URL patterns are changed over time so finding the correlation between old and new patterns is almost impossible. Hence, this paper proposed malicious URL detection using multilayer Convolutional Neural Network (CNN). The proposed model first considered one layer of CNN. After that, to improve the accuracy, a two-layer of CNN will be used. The achieved result illustrated that malicious website detection accuracy is enhanced 89% to 91% when the model uses two layers of CNN.