Metaheuristic Firefly and C5.0 Algorithms Based Intrusion Detection for Critical Infrastructures

Wireless Sensor Networks (WSN) are groups of stand-alone gadgets that typically feature one or more sensors (for example, light level, temperature), with relatively limited computing capabilities, and a wireless connection that enable interaction with a base station. Today, WSN is being implemented within critical infrastructures such as connected vehicles, drones, smart cities, smart grids, and surveillance systems. The major issue of WSN is that they are primarily focused on security issues linked to packet transfer across network's multiple sensor nodes. Intrusion detection is essential due to the growing importance of WSN security. To address this flaw in WSN, an effective wrapper feature selection founded on the Firefly algorithm (FFA) is developed for the selection of significant attributes in this paper. This wrapper-based feature selection solution reduces time consumption to a higher extent while also increasing the network's lifetime and scalability. In the first phase of this work, data preprocessing was performed with a minimum-maximum normalization approach, subsequently, FFA was used for feature dimensionality reduction and C5.0 for the classification. The simulations were done using the UNSW-NB1S benchmark data, and the suggested firefly with C5.0 (FFA-C5.0) has an accuracy of 98.7%.