Explainable AI-based innovative models for intrusion detection in Big Data WSN

Strong security measures are now more important than ever due to the quick growth of wireless sensor networks in sectors like smart cities, healthcare, and agriculture. In order to protect these networks from malevolent assaults, intrusion detection systems (IDS) are essential. However, network operators find it challenging to comprehend the system’s decision-making process due to the lack of transparency in traditional IDS. To address this issue, we propose an explainable artificial intelligence based intrusion detection method for large-scale WSNs. The goal of this research is to combine machine learning models with explain-ability frameworks like SHAP and LIME in order to efficiently identify and clarify anomalies in network data. In order to detect different kinds of attacks, we train and assess a variety of machine learning classifiers, such as decision trees, support vector machines, and ensemble approaches, using the popular KDD Cup 99 dataset. The XIA frameworkS assist discover important aspects that aid in attack detection and offer insights into the model’s decision-making process. Additionally, the model’s durability and scalability make it a feasible option for implementation in extensive WSNs, particularly in agriculture, where network security is essential to guaranteeing the dependability of IoT-based monitoring systems. The results have demonstrate the efficacy of the XAI-based intrusion detection system in attaining high accuracy, precision, recall, and F1-score while maintaining decision-making transparency. This method not only enhances intrusion detection but also provides network administrators with the means to decipher and verify the system’s predictions, increasing confidence in automated security systems.