article · IEEE Access
Fifth-generation networks in smart cities deploy intelligent access control across services like online banking and identity authentication, creating a need to secure biometrics against tampering. A deep learning framework addresses this by detecting biometric alterations to distinguish between pristine, adulterated, and fake inputs. The system utilises convolutional neural networks alongside a hybrid configuration that combines convolutional neural networks with convolutional long short-term memory networks. These models evaluate biometric samples and generate a three-tier probability indicating the likelihood of tampering. Simulation-based testing indicates that the detection accuracy matches that of advanced baseline methods. Furthermore, the architecture exhibits superior performance when identifying central rotation alterations in fingerprints, offering a mechanism to enhance identity safeguards across smart city environments.
Smart city applications increasingly depend on biometric identification to manage secure transactions and access control. If biometric records are manipulated, the integrity of services like online banking can be compromised. Providing reliable methods to spot adulterated and fake inputs helps secure digital authentication systems and protects individual identities against emerging cyber threats.
The method is aimed at identity authentication, online banking, and cyber security systems within smart city infrastructure. Potential users include municipal network operators, financial institutions, and security technology providers. Given that the system has been tested using simulation-based experiments, it currently represents an applied research stage that requires further development and testing in operational network environments before direct commercial deployment.
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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.
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DOI: 10.1109/access.2021.3088341
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