Version 1
: Received: 25 September 2023 / Approved: 26 September 2023 / Online: 26 September 2023 (10:51:48 CEST)
How to cite:
M. Almasabi, A.; Khemakhem, M.; Harbaoui, A.; Eassa, F.; B. Alkodre, A.; Jambi, K.; Abi Sen, A.A. A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning. Preprints2023, 2023091742. https://doi.org/10.20944/preprints202309.1742.v1
M. Almasabi, A.; Khemakhem, M.; Harbaoui, A.; Eassa, F.; B. Alkodre, A.; Jambi, K.; Abi Sen, A.A. A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning. Preprints 2023, 2023091742. https://doi.org/10.20944/preprints202309.1742.v1
M. Almasabi, A.; Khemakhem, M.; Harbaoui, A.; Eassa, F.; B. Alkodre, A.; Jambi, K.; Abi Sen, A.A. A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning. Preprints2023, 2023091742. https://doi.org/10.20944/preprints202309.1742.v1
APA Style
M. Almasabi, A., Khemakhem, M., Harbaoui, A., Eassa, F., B. Alkodre, A., Jambi, K., & Abi Sen, A.A. (2023). A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning. Preprints. https://doi.org/10.20944/preprints202309.1742.v1
Chicago/Turabian Style
M. Almasabi, A., Kamel Jambi and Adnan Ahmed Abi Sen. 2023 "A Smart Framework to Detect Threats and Protect Data of IoT based on Machine Learning" Preprints. https://doi.org/10.20944/preprints202309.1742.v1
Abstract
Abstract: 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.
Keywords
Internet of Things (IoT); Security; machine learning; fog computing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
