AI-Driven Privacy Analysis of IoT Services in Edge Computing
Maki Mahdi Abdulhasan, Ahmed Hussain, Mariam Fadel Ali, Mustafa Zuhaer Nayef Al-Dabagh, Jawad Radhi Rustum Al-Assal · 2024
Inevitably, IoT equipment generates more data, but fog computing's concept of transferring all data to the cloud for computation has failed to meet real-time needs. Cutting-edge edge computing delivers all data from the cloud to edge devices, enhancing service quality for IoT applications that need delay. However, Edge devices are more vulnerable to attacks than endpoint technologies due to low processing power and storage. Edge computing IoT services face growing dangers, making security and privacy solutions crucial. This article proposes AI-SPM to optimally solve IoT security and privacy issues in edge computing devices. AI's better learning skills help the proposed system detect harmful intrusions more precisely. The proposed solution was compared to other IoT security and privacy management methods to prove its efficacy. The proposed framework paradigm updates security and gives edge computing devices the most robust defences against IoT service cyberattacks. The AI-SPM achieved 99.54% consumer fulfilment, 99.88% security, 99.98% system reliability, and 5.34% latency compared to other techniques.