Cyber Edge Intelligent Intrusion Detection Framework For UAV Network Based on Random Forest Algorithm
Vivian Ukamaka Ihekoronye, Simeon Okechukwu Ajakwe, Dong‐Seong Kim, Jae Min Lee
Abstract
The synchronization of swarms of drones (also known as unmanned aerial vehicles (UAV)) in a network can be attributed to their high mobility and maneuverability capabilities, making them deployable for time-critical operations such as security surveillance, disaster management, and search and rescue operations. However, the resource constraints of these flying robots are limitations to their functionalities. Likewise, the neglect of the security status of this network significantly promotes attacks by invaders, thus, thwarting the mission of this network. In this study, mobile edge computing (MEC) technology and anomaly-based intrusion detection scheme are leveraged to curb these challenges using an optimized Random Forest (RCSV) model embedded in dedicated UAV-MEC servers. The selection of prominent features and hyperparameters for modeling an optimized attack predictor is enabled by Pearson correlation coefficient (PCC) and randomized search cross-validation techniques. Also, the training and evaluation of the proposed model were achieved using intrusion detection data set (CICIDS2017 data set) comprised of complex network attack types. The simulation results obtained by the model in the detection and classification of the different attacks in the network (accuracy = 99.87%, precision = 99.32%, recall = 98.81 % and F1-score = 99.06%) shows its superiority over other optimized machine learning models and some existing models utilized in previous research.