# PRISMA on Machine Learning Techniques in Smart City Development
## 3 Results
### Security/Cybersecurity (4 Papers)  
The increasing reliance on digital infrastructure in smart cities brings with it significant security challenges. Ensuring the cybersecurity of smart city systems is paramount to protecting sensitive data, maintaining public safety, and preventing disruptions. Thissection examines the role of machine learning in enhancing cybersecurity measures within smart cities, highlighting key studies that address the detection and mitigation of cyber threats. The insights gained from these studies are crucial for developing robust security frameworks that safeguard urban infrastructures. First, we introduce the challenges in deploying smart cities, emphasizing issues like sensor data integration, fraudulent activities, and security. One can observe the threat of surveillance capitalism to individual privacy, and a study proposes a novel consensus protocol for a permissioned blockchain system to address these challenges [41]. The dataset that emerged from the PRISMA analysis highlights the rising incidence of fraud in debit/credit card transactions, categorizing it into different types based on the perpetrators' characteristics. PVarious fraud detection methods, including decision trees, rule-induction techniques, support vector machines, logistic regression, artificial neural networks, and meta-heuristics, are explored [27; 63]. Different approaches, such as meta-classifier-based models and neural network-based techniques, are examined for fraud detection in cloud and IoT-based environments. It is discussed that the application of Dempster-Shafer theory, Bayesian learning, and rule-based filtering for fraud detection would be suitable. Also, the data compare different deception detection techniques, highlighting their pros and cons in cloud and IoT settings. A novel LR-n-fold intelligent machine learning technique is introduced for detecting fraudulent transactions in smart societies. The proposed method involves data collection, reformation, and feature mining, with a focus on tracing online cheating cases. Logistic regression, along with n-fold model selection, is employed for analysis, and the performance is compared with other existing techniques. The study uses datasets from European banks and addresses challenges like dataset imbalance through techniques like synthetic minority over-sampling. The proposed LR n-fold intelligent machine learning approach demonstrates high reliability and accuracy in detecting fraud in smart societies. Performance comparisons with other methods, including random forest-based decision tree models, are discussed, acknowledging certain limitations, especially in handling abrupt increases in datasets [27; 63]. Urgency is also emphasized for transaction prioritization to manage network traffic effectively. The protocol utilizes a permissioned blockchain with dynamic leader election, a modified true random number generator, and a sophisticated machine learning algorithm [41]. Moreover, the study delves into the intricacies of transaction prioritization mechanisms, the blockchain-based smart city design, overall procedures of the consensus algorithm, leader election, block creation, and peer prediction-based feedback collection. Crucially, the security analysis comprehensively addresses potential threats such as majority attacks, DDoS attacks, replay attacks, and empty block attacks. The experimental evaluation section further validates the protocol's prowess through simulated data analysis involving 1000 nodes, shedding light on the algorithm's accuracy and its responsiveness to various parameters, including the number of peers, trustworthiness score, number of blocks generated, and history of vote-outs on leader candidate prediction values [41]. The dataset also discusses the growing influence of the Internet of Things (IoT) in the development of smart cities, accompanied by an increased risk of cybersecurity threats.