Ensuring Zero Trust Security in Consumer Internet of Things Using Federated Learning-Based Attack Detection Model
Ali M. Al-Sharafi, Fatma S. Alrayes, Nuha Alruwais, Mohammed Maray, Asma Alshuhail, Abdulbasit A. Darem, Shoayee Dlaim Alotaibi, Mohammed Abdullah Al-Hagery
Abstract
The rising adoption of Consumer Internet of Things (CIoT) devices brings heightened concerns about security vulnerabilities and privacy breaches. Given their connection to daily routine and data collection abilities, it is vital to protect individual data against unauthorized entry and possible leaks proactively. Data protection’s maximum communication and storage charge will gradually challenge the typical ecology of integrated over-the-cloud learning and handling for IoT environments. Federated learning (FL) is developed as the most reasonable method for this challenge. In FL, training data-driven machine learning (ML) approaches is a form of association among several users with no need for data carried to a fundamental point; therefore, improving communication and storage charge offers a great degree of consumer-level secrecy. FL is an advanced ML that performs appropriately by inherently prioritizing confidentiality, circumventing requirements for centralized data collection, and supporting the defence. FL improves security in IoT by enabling decentralized model training without centralizing sensitive data, preserving privacy, and reducing communication costs. Integrating FL into attack detection models ensures zero-trust security, minimizing vulnerabilities and enhancing protection against cyber threats while keeping data on-device. This study develops an enhanced artificial gorilla troop optimizer with an FL-based attack detection and classification (EGTO-FLADC) approach on the CIoT environment. The EGTO-FLADC approach aims to improve security in the CIoT environment via FL and the attack detection process. Primarily, the EGTO-FLADC technique utilizes the FL model for collaborative training of combined models with many devices with no data-sharing process. In addition, the EGTO-FLADC technique uses a min-max scalar for data normalization. For attack detection, the EGTO-FLADC technique employs the temporal convolution networks with a gated recurrent unit (TCN-GRU) model and its hyperparameters are chosen by the EGTO model. The EGTO technique involves the integration of the conventional GTO model with the marine predator’s algorithm (MPA) model. The performance evaluation of the EGTO-FLADC approach is examined under the EdgeIIoTset dataset. The experimental validation of the EGTO-FLADC approach portrayed a superior accuracy value of 97.11% over existing models.