Deep Quantile Regression Neural Network with GAOA and Multifactor Graphical-Based Authentication for Integrated Data Security in Smart City Applications

doi:10.23940/ijpe.26.11.p4.655668

Abstract

Abstract: Smart City (SC) refers to an innovative idea that makes use of Information and Communication Technology to organize cities and create digitally connected urban environments. The extensive use of Internet of Things (IoT) devices and cutting-edge technologies makes network security in smart cities essential. Establishing and executing strong security procedures often requires public-private collaborations. To improve the privacy and security of smart city networks, various network protocols and systems have emerged beyond conventional measures. However, these solutions face significant limitations, including high algorithmic complexity, longer processing times, reduced maintainability, and a lack of robust authentication and verification mechanisms. To overcome these drawbacks, a Deep Quantile Regression Neural Network (DQRNN) with a multifactor graphical authentication system is developed for improving smart city security. Initially, in smart cities, data is collected via sensors and transmitted to a cloud server for centralized storage. To access data from the cloud server, a Multifactor Graphical-based Authentication Scheme (MGAS) is used, which safeguards data integrity and prevents unauthorized access. For intrusion detection during transmission from the user to the cloud, features of the transmitted data packets are collected and subjected to various processing steps. Initially, the data undergoes pre-processing using Aggregation-based Group Normalization (AGN) and an Edge Generation Graph Auto-Encoder (EGGAE) to improve data quality. Feature extraction then follows using Regularization-based Locality Sensitive Discriminant Analysis (RLSDA) to isolate the features that are most relevant for intrusion detection. The data is then classified using a DQRNN tuned with the Giant Armadillo Optimization Algorithm (GAOA) to detect intruders. The proposed model achieves an accuracy of 96.1%, a selectivity of 96.5%, and a precision of 96.4%; the execution time for the MGAS is 1.2 s and the security level is 93.8%. Consequently, the proposed methods are highly suitable for real-time smart city applications, enhancing network security through robust authentication and intrusion detection.

Key words: deep quantile regression neural network, smart cities, internet of things, regularization-based locality sensitive discriminant analysis, giant armadillo optimization algorithm, cloud server

Nidhi Gupta, Neeta Singh, and Aarti Gautam Dinker. Deep Quantile Regression Neural Network with GAOA and Multifactor Graphical-Based Authentication for Integrated Data Security in Smart City Applications [J]. Int J Performability Eng, 2026, 22(11): 655-668.

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