An Intrusion Detection System for Edge-Envisioned Smart Agriculture in Extreme Environment

The deployment of Internet of Things (IoT) systems in smart agriculture (SA) operates in extreme environments, including wind, snowfall, flooding, landscape, and so on for collecting and processing real-time data. The increased connectivity and broad adoption of IoT devices with low-power communications on farmland support farmers in making data-driven decisions using various artificial intelligence (AI) techniques. Furthermore, in such an environment, edge computing is also utilized to provide computationally intensive, latency-sensitive, and bandwidth-demanding services at the edge of the network. However, protecting edge-to-Things in the extreme environment of SA is challenging, due to the volume of data, and also attackers exploit network gateways to perform distributed denial of service (DDoS) attacks. Motivated by the aforementioned challenges, we develop a novel deep learning (DL)-based intrusion detection system (IDS) for edge-envisioned SA in extreme environments. Specifically, a hybrid approach is developed by combining bidirectional gated recurrent unit, long-short-term memory with softmax classifier to detect attacks at the edge of the network. To allow faster learning, the proposed IDS employs the truncated backpropagation through time (TBPTT) approach to handle lengthy sequences of network data. Furthermore, we suggest an attack scenario with deployment architecture for the proposed IDS in the extreme environment of SA. Extensive experiments using three publicly available datasets, namely, CIC-IDS2018, ToN-IoT, and Edge-IIoTset prove the effectiveness of the proposed IDS over some traditional and contemporary state-of-the-art techniques.