Privacy and Security Enhancement of Smart Cities using Hybrid Deep Learning-enabled Blockchain

Abstract

The emergence of the Internet of Things (IoT) accelerated the implementation of various smart city applications and initiatives. The rapid adoption of IoT-powered smart cities is faced by a number of security and privacy challenges that hindered their application in areas such as critical infrastructure. One of the most crucial elements of any smart city is safety. Without the right safeguards, bad actors can quickly exploit weak systems to access networks or sensitive data. Security issues are a big worry for smart cities in addition to safety issues. Smart cities become easy targets for attackers attempting to steal data or disrupt services if they are not adequately protected against cyberthreats like malware or distributed denial-of-service (DDoS) attacks. Therefore, in order to safeguard their systems from potential threats, businesses must employ strong security protocols including encryption, authentication, and access control measures. In order to ensure that their network traffic remains secure, organizations should implement powerful network firewalls and intrusion detection systems (IDS). This article proposes a blockchain-supported hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) to provide privacy and security for smart city users and systems. Blockchain is used to provide trust, and CNN enabled with KPCA is used for classifying threats. The proposed solution comprises three steps, preprocessing, feature selection, and classification. The standard features of the datasets used are converted to a numeric format during the preprocessing stage, and the result is sent to KPCA for feature extraction. Feature extraction reduces the dimensionality of relevant features before it passes the resulting dataset to the CNN to classify and detect malicious activities. Two prominent datasets namely ToN-IoT and BoT-IoT were used to measure the performance of this anticipated method compared to its best rivals in the literature. Experimental evaluation results show an improved performance in terms of threat prediction accuracy, and hence, increased security, privacy, and maintainability of IoT-enabled smart cities.

Improving the Robustness of IoT-Powered Smart City Applications Through Service-Reliant Application Authentication Technique

“…The performance metrics include access success rate, computation overhead, synchronisation failure, false rate, and authentication delay. The suggested SRAA for the metrics above is used in conjunction with the current ChRMAC [20], PrivySharing [28], CNN-KPCA [41 ]and HAC [18] techniques for comparative analysis. The authentication delay for the proposed work decreases by varying access requests and the number of devices.…”

“…In smart city applications, the proposed is applicable, and the performance based on the accuracy, penetration rate and overhead is compared with existing privacy techniques. To ensure the safety and privacy of smart city users and systems, Joseph Bamidele Awotunde et al [41] proposed a hybrid Convolutional Neural Network (CNN) with Kernel Principal Component Analysis (KPCA) that is powered by blockchain technology. The findings of the experimental assessment demonstrate that smart cities enabled by the Internet of Things perform better in terms of the accuracy of danger predictions, leading to enhanced privacy, security, and maintainability.…”

Neurotechnologies Applied to Society's Perception of Cyber-Physical Systems (CPS) in Smart Cities

“…CPS-based solutions for emergency management and disaster response in smart cities Almeida, 2023;Alshahrani & Prati, 2023;Xia et al, 2023;Aurangzeb et al, 2023;Aurangzeb et al, 2023;Sangaiah et al, 2023;Awotunde et al, 2023;Wenjuan Li et al, 2023;Shao et al, 2023 He et al, 2020;Vogeley & Ryder, 2023;Panda et al, 2020 informed decisions. Nowadays, with the continuous integration of big data, artificial intelligence, and cloud computing technologies, there are growing demands and specific requirements for data exchange in sustainable smart cities (Hsu et al, 2023).…”

“…The adoption of information and communication technologies for smart city development has increased the risk of cyber threats, emphasizing the need to review the cybersecurity governance model. In order to safeguard a decentralized setup like smart cities, the Collaborative Intrusion Detection System (CIDS) has become a conventional security mechanism to protect different types of computer networks (Awotunde et al, 2023), especially decentralized computing platforms like the Internet of Things (IoT).…”