Quantum Machine Learning-Enhanced Network Intrusion Detection Systems for IoT in Agriculture: A Sustainable Approach

The increasing adoption of Internet of Things (IoT) devices in agriculture has revolutionized farming practices. However, the interconnected nature of these devices also makes them vulnerable to cyber-attacks, compromising data security and operational efficiency [20, 28] (Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices, Eskandari M, Janjua ZH, Vecchio M, Antonelli F, Passban IDS (2020) An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices, in IEEE Internet of Things Journal 7(8):6882–6897. https://doi.org/10.1109/JIOT.2020.2970501 . Setiadi et al. (2024) Computers 13:191). Traditional Intrusion Detection Systems (IDS) often struggle to effectively detect and respond to sophisticated cyber threats, particularly those that exploit emerging vulnerabilities in IoT devices. This paper proposes a novel approach to enhance network intrusion detection in agricultural IoT systems by leveraging the power of Quantum Machine Learning. By harnessing the unique capabilities of quantum computing, we aim to develop a more robust and efficient IDS capable of detecting complex and evolving cyber threats. The proposed system leverages quantum algorithms to analyze network traffic data, identify anomalies, and classify malicious activities with higher accuracy and speed compared to classical machine learning techniques. Furthermore, we explore the sustainability aspects of quantum computing in the context of agricultural IoT. By reducing computational energy consumption and accelerating the training and inference processes, quantum-enhanced IDS can contribute to a more sustainable and environmentally friendly agricultural sector.