IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments.

The widespread use of IoT devices has introduced new challenges, particularly related to security and privacy threats such as unauthorized data access and device breaches. The high demand for these devices, coupled with the lack of strong security and privacy systems for user data, highlights the need for more effective approaches and solutions to address these issues. In this study, we propose a framework designed to audit, test, and detect potential vulnerabilities within IoT environments and their applications. The main components of the proposed framework include a machine-learning algorithm for data classification and attack detection. The framework also introduces two additional features calculated by the edge computing layer (fog layer) to enhance the accuracy of the classification algorithm. The algorithm uses multiple classification models to improve accuracy. To assess our classification algorithm’s efficiency, we implemented it using a real IoT dataset known as the TON_IoT dataset. The results demonstrated the accuracy, efficiency, and precision of our approach in detecting and mitigating potential vulnerabilities and threats like Tamper Data, Injection, Denial of Service, and Backdoor attacks. We achieved an accuracy rate of over 98%. Our findings offer valuable recommendations to enhance security and privacy within IoT systems, and we also explore emerging trends in these areas.

The Internet of Things (IoT) has witnessed rapid and widespread adoption across various domains, including transportation, healthcare, education, agriculture, urban planning, smart homes, and more. Despite its transformative potential, this pervasive deployment of IoT devices has introduced new challenges, particularly concerning security and privacy threats such as unauthorized data access and device breaches. The excessive usage of these technological devices, coupled with the absence of robust security and privacy systems for user data, calls for a comprehensive approach to address these issues. In this study, we propose a novel framework designed to analyze, audit, test, and detect potential vulnerabilities within IoT environments and applications. The central components of the proposed framework include a machine learning algorithm for data classification and attack detection, along with the integration of Blockchain technology to enhance security measures. Specifically, the framework performs an in-depth analysis of user data to identify potential security or privacy vulnerabilities. Additionally, it conducts rigorous testing of smart services and automated data-collecting devices. To evaluate the effectiveness of our classification algorithm, we conducted a comprehensive implementation on a real-world IoT dataset. The results showcased the efficiency and accuracy of our approach in detecting and mitigating potential threats. Furthermore, based on our research findings, we provide valuable recommendations for enhancing security and privacy in IoT ecosystems. We also highlight emerging trends in the security and privacy domains, which can serve as valuable insights for researchers and practitioners. In conclusion, our proposed framework offers a robust and proactive approach to address the security and privacy challenges, such as unauthorized data access and device breaches posed by the widespread adoption of IoT devices. By combining machine learning algorithms and Blockchain technology, we contribute to safeguarding user data and fostering a secure environment for IoT applications. This study lays the groundwork for further advancements in the realm of IoT security and privacy, ensuring a safer and more resilient IoT landscape for the future.