Enhancing IoT Security: Proactive Phishing Website Detection Using Deep Neural Networks Case study: Smart Home
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Keywords
Smart Home, Phishing, URL Classification, Security, Deep Neural Network
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.
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References
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Habiba Bouijij, SSLab ENSIAS, Mohammed V University in Rabat, Morocco
Habiba BOUIJIJ is a PhD student at the Smart Systems Laboratory at ENSIAS - Mohamed V University in Rabat, Morocco. She is also a Senior high school teacher of computer science. Additionally, she serves as a Trainer in Information and Communication Technology (ICT) at the Regional Center for Education and Training Professions in Khemisset, Morocco. She has also worked as a major supervisor for on-going training in Python for secondary school computer science teachers and specialized Microsoft Office training.
Amine Berqia, SSLab ENSIAS, Mohammed V University in Rabat, Morocco
Amine BERQIA Dr. Amine BERQIA is Full Professor at ENSIAS, University Mohammed V in Rabat, Morocco. He is Head of Communication Networks Department. He teaches computer sciences, communication networks, operating systems, security, networks security, wireless and mobile networks. He is a member of the teaching team of the engineering degree in information systems security. Dr. Amine BERQIA is member of Rabat IT Center and Smart Systems Research Lab. He holds a Ph.D. degree in Computer Sciences from the University of Dijon, France. He was Assistant Professor at University of Geneva and Coordinator of the Swiss Virtual Campus Project VITELS from 2000 to 2003. He published more than 50 papers in journals and conferences in the areas of Networks performance, Security and New Generation of learning. In 2012 Dr. Amine Berqia was awarded by IEEE Education Society in recognition and appreciation for his valued services and contributions as 2012 IEEE Educon conference Organizing Committee Chair. In 2013 Dr. Amine Berqia was awarded by iNEER. In 2019, Dr. Amine Berqia was awarded by IEEE Standards Association for his valued services and contributions for the development of IEEE Standard 1876.
