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This study aims to examine the influence of Internal Control Effectiveness and Accounting Practices on the level of Fraudulent Activities within companies. The data analysis reveals that both independent variables have a significant impact on the dependent variable. The Internal Control Effectiveness shows a positive and significant correlation with Fraudulent Activities, while Accounting Practices exhibit a negative and significant correlation. Partial T-tests confirm that each variable individually affects Fraudulent Activities. Additionally, the simultaneous F-test indicates that Internal Control Effectiveness and Accounting Practices together have a significant impact on Fraudulent Activities. Multicollinearity and heteroscedasticity tests show no serious issues related to classical assumptions, and the autocorrelation test indicates no autocorrelation in the model residuals. These findings suggest that improving internal controls and accounting practices can significantly reduce the risk of fraud within companies.

The rapid development of smart city initiatives has significantly increased the adoption of Internet of Things (IoT) technologies to enhance urban services, infrastructure efficiency, and quality of life. However, the large-scale deployment of interconnected IoT devices also introduces critical cybersecurity challenges, including unauthorized access, data breaches, and system vulnerabilities. This study aims to develop an integrated IoT security management model to improve cybersecurity resilience in smart city environments. The research adopts a Design Science Research (DSR) approach, which involves problem identification, literature analysis, model design, implementation, and evaluation. The proposed model incorporates key security components such as Identity and Access Management (IAM), device authentication, secure communication through encryption, firmware and patch management, and continuous monitoring with intrusion detection mechanisms. The model is evaluated through simulation in smart city scenarios, including transportation systems, environmental monitoring, and energy management. The results demonstrate significant improvements in security performance, with increases in threat detection rate, vulnerability reduction, access control effectiveness, and system stability under attack conditions. Quantitative analysis shows improvements of up to 37% compared to conventional approaches, indicating the effectiveness of the proposed model in mitigating IoT-related cybersecurity risks. This study contributes by providing a comprehensive and scalable framework for IoT device security management, which can be applied to enhance the reliability and sustainability of smart city systems. Future research is recommended to validate the model in real-world implementations and integrate advanced technologies such as artificial intelligence for predictive threat detection.