Radial Basis Function Neural Networks (RBFNN) for Secure Intrusion Detection in the Internet of Drones (IoD)

Abstract

With the rapid rise of the Internet of Things (IoT), drones are being used in various applications such as surveillance and delivery services. Despite this, there are serious security threats as well. This study proposes an innovative secure intrusion detection platform for the Internet of Drones (IoD) framework in order to address this problem. We use Radial Basis Function Neural Networks (RBFNN) and Non-Linear Partial Differential Equations (NL-PDEs) technique to enhance the intrusion detection capabilities. In addition the incorporation of block-chain technology fortifies the security and transparency of the system, ensuring the integrity of captured intrusion data. The proposed technique has been rigorously tested and evaluated to determine its accuracy in identifying malicious attacks and unauthorized access scenarios. Through this methodology, the Internet of Drones (IoD) security framework is strengthened, thereby mitigating potential risks derived from their widespread use. The main objective of this study is to make it easier for drone technology to be adopted and used safely and securely in a variety of contexts and industries.