An Intrusion Detection System Using Machine Learning to Secure the Internet of Drones

In recent years, the growing deployment of unmanned aerial vehicles, often known as drones, has transformed several industries, including agriculture, logistics, and surveillance. However, new security concerns have arisen as a result of the widespread adoption of drone technology. Using machine learning for intrusion detection, this research presents a novel method to strengthen drone network security. To simulate potential threats in the IoD ecosystem, the proposed model draws from a large and varied CIC IoT dataset. The model is trained to recognize the many forms of cyberthreats that drones may face throughout their missions using supervised learning. This dataset covers a wide variety of attacks, guaranteeing the model's robustness against a wide range of security breaches. The research trains models using supervised machine learning techniques on the CIC IoT dataset to differentiate between various cyberrisks to drones. Metrics like accuracy, precision, recall, and F1-score are used to rigorously assess the models. Extensive trials show that the machine learning models can successfully identify and classify various forms of IoD attacks, with an average accuracy of over 90% being achieved through their use. The suggested intrusion detection framework has promising real-time threat detection and mitigation potential. The model's efficacy in identifying and classifying assaults has been demonstrated through extensive testing, with an average accuracy rate of 92% across a wide range of attack types. Furthermore, the model's potential to identify threats in real time aids to quick threat mitigation, thereby enhancing the overall security of the IoD environment. This research shows the role that machine learning can play in protecting the airspace, providing perspective on the prospects for securing and developing the Internet of Drones in the future.