Federated Learning and Convolutional Autoencoder for Robust Anomaly Detection in Agricultural IoT - Khacha, Amina; Saadouni, Rafika; Harbi, Yasmine; Gherbi, Chirihane; Aliouat, Zibouda; Harous, Saad (2024 International Conference of the African Federation of Operational Research Societies (AFROS), 2024)
Security is a vital challenge that must be addressed in the Agricultural Internet of Things (AIoT) to ensure enhanced productivity, quality, and resilience. Robust security measures are essential to protect sensitive data, prevent unauthorized access, and safeguard against cyberthreats, ultimately contributing to agricultural operations' overall efficiency and reliability. One solution to improve security is the integration of intelligent Intrusion Detection Systems (IDS) using deep learning methods. However, traditional deep learning training methods require centralizing data and compromise client privacy. Federated learning (FL) addresses this challenge by enabling privacy-preserving and cooperative training of deep learning models among clients. This paper presents an anomaly-based IDS using a Convolutional Autoencoder (CAE) and FL. We assess our proposed solution using a new real and large dataset, CICIoT-2023. We evaluate our model with independent and identically distributed (IID) and non-IID data distributions to further enhance performance. Our model demonstrates superior performance compared to centralized training, achieving a recall of 99.99% and an F1score of 99.40%.