Abstract:
Wireless sensor networks (WSNs) face significant challenges related to dependability, integrity, and confidentiality due to their widespread deployment. Intrusion detection emerges as a pivotal active defense technology to fortify the security of WSNs. Given the unique characteristics of WSNs, achieving a balance between precise data transmission and the limited energy resources of sensors, as well as between effective detection and optimal utilization of network resources, becomes imperative. In this research, we propose a fog computing-based intrusion detection system (IDS) tailored for a smart power grid environment. The primary focus of this article is to elucidate the application of IDS within the context of a smart grid. We present a novel stacked model based on ensemble learning algorithms, adept at accurately delineating the interconnections among fog nodes susceptible to cyber-attacks. To validate the efficacy of our approach, we conduct a series of comparative experiments utilizing the KDD-Cup-99 dataset with cross-validation. Our findings reveal that the Fog-IDS and Stacking-based approaches significantly enhance the performance of the IDS. Through simulations, we substantiate that our proposed approach effectively analyzes fog nodes using the deployed IDS, enabling the prediction of energy requirements at refill stations. Achieving an impressive accuracy rate exceeding 99.837%, MCC rate of 99.73%, and F1-score rate of 99.827%, our study underscores the robustness and reliability of the Fog-IDS and Stacking-based methods in augmenting IDS performance in WSNs.
