Securing Iot Networks Against Fraud Using Deep Radial Basis Function Neural Networks

Abstract

The rapid proliferation of Internet of Things (IoT) devices has led to an increased risk of security frauds within IoT networks. Traditional security measures often fall short in addressing the dynamic and diverse nature of these frauds. The heterogeneity of IoT devices and their intricate communication patterns pose significant challenges in identifying potential security breaches. Conventional security approaches struggle to adapt to the evolving fraud landscape, necessitating the exploration of advanced techniques. Deep Radial Basis Function (RBF) networks offer promise in capturing the complex relationships inherent in IoT data, enabling more effective fraud detection. While existing literature has explored various machine learning approaches for IoT security, the integration of Deep RBF networks specifically in this context remains underexplored. This research aims to bridge this gap by investigating the efficacy of Deep RBF networks in identifying anomalies within IoT networks, addressing the unique challenges posed by the interconnected and diverse nature of IoT devices. The study involves the collection of a comprehensive dataset encompassing normal and anomalous IoT network activities. Feature selection focuses on key parameters such as device communication patterns, data traffic, and system behavior. Deep RBF networks are then trained on this dataset to learn and distinguish normal behavior from potential security frauds. The methodology combines the strengths of Deep Learning with the adaptability of RBF networks to capture nuanced patterns indicative of security vulnerabilities. The results demonstrate the effectiveness of Deep RBF networks in accurately detecting security frauds in IoT networks. The model exhibits a high level of sensitivity to anomalous activities, showcasing its potential as a robust tool for enhancing the security posture of IoT environments.