Enhanced smart farming security with class-aware intrusion detection in fog environment

Abstract

The adoption of the internet of things (IoT) in smart farming has enabled real-time data collection and analysis, leading to significant improvements in productivity and quality. However, incorporating diverse sensors across large-scale IoT systems creates notable security challenges, particularly in dynamic environments like Fog-to-Things architectures. Threat actors may exploit these weaknesses to disrupt communication systems and undermine their integrity. Tackling these issues necessitates an intrusion detection system (IDS) that achieves a balance between accuracy, resource optimization, compatibility, and affordability. This study introduces an innovative deep learning-driven IDS tailored for fog-assisted smart farming environments. The proposed system utilizes a class-aware autoencoder for detecting anomalies and performing initial binary classification, with a SoftMax layer subsequently employed for multi-class attack categorization. The model effectively identifies various threats, such as distributed denial of service (DDoS), ransomware, and password attacks, while enhancing security performance in environments with limited resources. By utilizing the Fog-to-Things architecture, the proposed IDS guarantees reliable and low-latency performance under extreme environmental conditions. Experimental results on the TON_IoT dataset reveal excellent performance, surpassing 98% accuracy in both binary and multi-class classification tasks. The proposed model outperforms conventional models (convolutional neural network (CNN), recurrent neural network (RNN), deep neural network (DNN), and gated recurrent unit (GRU)), highlighting its superior accuracy and effectiveness in securing smart farming networks.