A hybrid feature selection method for anomaly detection using shallow and deep ANN classifiers in smart farming

Abstract

Smart farming systems, while enhancing agricultural productivity, are increasingly vulnerable to cyber threats due to their reliance on interconnected devices and networks. However, existing Intrusion Detection Systems (IDS) often suffer from high computational costs and suboptimal detection accuracy due to irrelevant features. To address this challenge, this study proposes a novel hybrid filter-based feature selection method designed to optimize feature selection for artificial neural network (ANN)-based IDS in smart farming environments. Unlike conventional methods that rely solely on chi-square, mutual information, or mean absolute deviation, the proposed method combines these techniques to leverage their complementary strengths. Furthermore, a comprehensive smart farming system was established to collect extensive data, creating a dedicated dataset named Smart-Farm-IDS for binary classification, distinguishing between normal operations and anomalies. Both shallow and deep ANN models were employed to detect these anomalies, with their performances compared in detail. Experimental results demonstrate that the proposed hybrid feature selection method enhances detection accuracy while reducing computational overhead compared to existing methods. This study offers a robust approach for improving the security and resilience of smart farming systems, providing a foundation for more secure agricultural operations.