Optimizing data privacy and security measures for critical infrastructures via IoT based ADP2S technique.

Area of Science:

• Computer Science
• Cybersecurity
• Artificial Intelligence

Background:

• Internet of Things (IoT) platforms handle sensitive data, making them susceptible to security risks.
• Protecting user data privacy in IoT is crucial, yet data loss and complexity are significant challenges.
• Existing security measures often struggle with the scale and dynamic nature of IoT environments.

Purpose of the Study:

• To propose an Attuned Data Protection with Privacy Scheme (ADP2S) for enhanced IoT security.
• To leverage a novel reptile search optimization algorithm for vulnerability detection and mitigation.
• To improve data protection, privacy, and reduce operational complexities in IoT applications.

Main Methods:

• Implementation of a reptile search optimization algorithm mimicking reptile hunting behaviors for vulnerability discovery.
• Development of a security scheme (ADP2S) that activates a 'reptile swarm' upon detecting a breach.
• Integration of elliptic curve cryptography and two-factor authentication for robust user and service provider authentication.

Main Results:

• The proposed ADP2S scheme improved breach detection and protection recommendations by 11.37% and 8.04%, respectively.
• Significant reductions were achieved in data loss (6.58%), estimation time (10.9%), and complexity (11.21%).
• The algorithm effectively merges exploration and exploitation stages by sharing search locations among candidates.

Conclusions:

• The reptile search optimization algorithm offers an effective approach to enhance IoT security and data privacy.
• ADP2S demonstrates a significant improvement in mitigating security risks and operational challenges in IoT environments.
• The scheme provides a scalable and efficient solution for protecting sensitive data in shared IoT infrastructures.