Optimizing data privacy and security measures for critical infrastructures via IoT based ADP2S technique - Xu, Zhenyu; Wang, Jinming; Feng, Shujuan; Othmen, Salwa; Lhioui, Chahira; Flah, Aymen; Slanina, Zdenek (Scientific reports, 2025)
The sensitive nature of the data processed by the critical infrastructures of a shared platform like the internet of things (IoT) makes it vulnerable to a wide range of security risks. These infrastructures must have robust security measures to protect the privacy of the user data transmitted to the processing systems that utilize them. However, data loss and complexities are significant issues when handling enormous data in IoT applications. This paper uses a reptile search optimization algorithm to offer attuned data protection with privacy scheme (ADP2S). This study follows the reptiles' hunting behaviours to find a vulnerability in our IoT service's security. The system activates the reptile swarm after successfully gaining access to explode ice. An attack of protection and authentication measures explodes at the breach location. The number of swarm densities and the extent to which they explore a new area are both functions of the severity of the breach. Service response and related loss prevention time verify fitness according to the service-level fitness value. The user and the service provider contribute to the authentication, which is carried out via elliptic curve cryptography and two-factor authentication. The reptile's exploration and exploitation stages are merged by sharing a similar search location across the initialized candidates. The proposed scheme leverages breach detection and protection recommendations by 11.37% and 8.04%, respectively. It reduces the data loss, estimation time, and complexity by 6.58%, 10.9%, and 11.21%, respectively.
Keywords: Optimization; Artificial intelligence; Big data; Algorithms; Internet of things