Optimized Edge-cCCN Based Model for the Detection of DDoS Attack in IoT Environment

In the context of the Internet of Things (IoT), safeguarding against Distributed Denial of Service (DDoS) attacks is critical. This paper introduces an Optimized Edge-cCNN (Convolutional Neural Network) Model designed for robust DDoS detection in IoT environments. The model employs two specialized CNN layers to identify distinct DDoS attack types. To enhance its performance, we utilize the Cuckoo Search algorithm to fine-tune hyperparameters effectively. Our approach demonstrates superior accuracy compared to existing methods while remaining lightweight, making it suitable for resource-constrained edge devices. Through rigorous evaluation, our model exhibits its effectiveness in real-time DDoS threat mitigation. The Optimized Edge-cCNN Model presents an innovative solution for enhancing IoT security, combining deep learning and optimization techniques to combat evolving DDoS attacks effectively.