Enhancing IoT Security: Proactive Phishing Website Detection Using Deep Neural Networks

Abstract

The Internet of Things (IoT) has proven its utility across various domains, including healthcare, agriculture, industry, and finance. It comprises Internet-connected devices that offer remote control capabilities. However, this very connectivity exposes these devices to potential cyberattacks. Cybercriminals can exploit the vulnerabilities in these devices by sending deceptive emails or text messages containing malicious links leading to hacker websites or destructive applications. This allows them unauthorized access to connected devices and the acquisition of sensitive personal information. Such malicious tactics are collectively known as phishing and pose one of the most prevalent threats. This article presents an innovative method that harnesses the power of a Deep Neural Network to accurately classify and proactively prevent phishing websites by analyzing their URLs. The method is demonstrated through a smart home use case, aiming to reinforce IoT security and safeguard users’ sensitive data by proactively identifying and preventing phishing attacks. By harnessing the power of the Deep Learning model, this innovative technique seeks to enhance online safety and protect users from potential cyber threats.