Treffer: A novel secured open standard framework for internet of things applications integrating elliptic curve cryptography and fog computing.

The internet of things (IoT) has revolutionized various fields by enabling seamless connectivity and data exchange among numerous devices. However, this interconnectivity introduces significant security challenges, particularly in ensuring data confidentiality, integrity, and authenticity. This study proposes a novel secure open standard framework for IoT applications, addressing these challenges through the integration of elliptic curve cryptography (ECC) and fog computing. The framework consists of three core components: secure device registration, data encryption within the fog gateway, and a robust mechanism for detecting man-in-the-middle (MITM) attacks. The unique aspect of the proposed method lies in its comprehensive approach to IoT security. Utilizing ECC, the framework ensures secure communication among resource constrained IoT devices, balancing encryption strength and efficiency. The integration of fog computing reduces latency and enhances processing efficiency by offloading intensive tasks from IoT devices to the fog layer. The MITM attack detection mechanism continuously monitors cryptographic keys and communication patterns, providing an additional layer of security against advanced cyber threats. The system was implemented and evaluated using the NS-3.26 network simulator and Python for data visualization. The experimental setup included 100 IoT devices, 25 users, a fog gateway, a datacenter, and a cloud server. Results demonstrate the framework's scalability and efficiency, with consistent throughput increases and balanced power consumption across varying IoT device numbers. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)