Fine-tuning CNN for Enhanced Security in WSN-Based Forest Fire Detection

In recent years, Wireless Sensor Networks (WSNs) have gained significant attention for their role in forest fire detection, offering early detection and real-time monitoring capabilities. WSNs are tailored to continuously monitor environmental parameters such as temperature, humidity, smoke, and gas concentrations, utilizing various sensors, communication modules, and a central monitoring system. However, their heterogeneous nature and unattended environments render WSNs vulnerable to diverse cyberthreats. Adversaries can exploit these vulnerabilities to manipulate smart devices remotely, posing serious risks to the integrity and resilience of forest ecosystems. Addressing these security concerns is crucial to ensuring uninterrupted monitoring of environmental parameters and effective forest fire detection. To mitigate these risks, we present a robust Intrusion Detection System (IDS) based on a Convolutional Neural Network (CNN) and Genetic Algorithm (GA), specifically designed to enhance the security of WSNs deployed for forest fire detection. Our proposed model involves hyperparameters optimization of the CNN model using GA. The experiments conducted on three public datasets, namely NSL-KDD, Edge-IoTset, and CICIoT-2023, demonstrate the effectiveness of our methodology in enhancing security, achieving a high accuracy of 99.26% and a low FPR of 0.00735% using NSL-KDD while achieving 100% accuracy and zero FPR using the two new realistic datasets Edge-IIoTset and CICIoT-2023.