Image-Based Dual Defense Strategy for Adversarially Robust IDS in Smart Agriculture

Abstract

The integration of Internet of Things (IoT) technologies into smart agriculture has significantly enhanced automation, monitoring, and productivity. However, these systems introduce critical cybersecurity vulnerabilities and generate heterogeneous data, including structured network traffic and image-based inputs. This requires an intrusion detection system (IDS) capable of handling multimodal data, particularly since adversarial attacks can manipulate inputs to evade traditional detection models. To address these challenges, this paper proposes a novel image-based IDS for smart agriculture environments. The system transforms network traffic into images and employs VGG16 for feature extraction, Binary Greylag Goose Optimization for feature selection, and a random forest for classification. It further integrates a dual defense strategy that combines a Convolutional Autoencoder Denoising (CAED) module with Adversarial Training (AT) to improve robustness against adversarial perturbations. The proposed solution is evaluated on the CICIoT2023 dataset in eight traffic classes under three white-box adversarial attacks. The IDS demonstrates strong resilience across all perturbation levels. For weak perturbations (ε=0.01), the dual defense achieves accuracies of at least 99.46%. Under moderate perturbations (ε=0.1), it maintains high performance with macro-averaged accuracies of at least 99.39%. Even under strong perturbations (ε=0.3), the system remains robust, attaining an accuracy of at least 96.50%. To assess generalization to real agricultural settings, the IDS is also tested using native crop images from the agricultural dataset. Under severe adversarial distortion (ε=0.3), the system maintains robustness, achieving a macro-averaged accuracy of at least 96.71%. These results confirm that the proposed multimodal IDS provides a resilient, adaptive security solution for smart agriculture networks facing advanced adversarial threats.