Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

IEEE Access

Lo'Ai Tawalbeh¹  Fathi E. Abd El-Samie²  Abdullah M. Iliyasu³  Ashraf A. M. Khalaf⁴  Ahmed Sedik⁵  Ahmed A. Abd El-Latif⁶  Mohamed Hammad⁷  Ghada M. El-Banby⁸ 

[1] Department of Computing and Cybersecurity, Cyber Security Research Centre, Texas A&x0026;Department of Electrical Engineering, Faculty of Engineering, Minia University, Minya, Egypt;Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt;Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt;Department of the Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt;Faculty of Computers and Information, Menoufia University, Shebin El-Kom, Egypt;M University at San Antonio, San Antonio, TX, USA;Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin El-Kom, Egypt;

关键词: Deep learning;    cyber threats;    biometric alteration detection;    cybersecurity;    5G network;    smart city;   

DOI  :  10.1109/ACCESS.2021.3088341

来源: DOAJ

【 摘 要 】

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, and cyber security. To assure secure transactions and to protect user’s identities against cybersecurity threats, strong authentication techniques should be used. The prevalence of biometrics, such as fingerprints, in authentication and identification makes the need to safeguard them important across different areas of smart applications. Our study presents a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we use deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a three-tier probability that a biometric has been tempered. Simulation-based experiments indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in secure smart cities.

【 授权许可】

Unknown