Optimized DDoS Detection in Smart Homes Using EPSO and Recurrent Transformer Networks

Abstract

The use of data encryption in smart cities is increasing by the day, and this creates a lot of problems because it is difficult to notice some kinds of cyberattacks, e.g., encrypted DDoS attacks. To address this issue, the study presents a novel framework that utilizes artificial intelligence for detection and prevention. This is made possible by using the Enhanced Particle Swarm Optimization (EPSO) algorithm to give priority to important data and tune up the parameters of the Recurrent Transformer Network (RTN). The combination helps not only in effectively identifying attacks but also in improving differentiation capabilities between regular benign and malignant encrypted traffic. It has also been proven through performance evaluation that this approach offers a superior ability to detect threats while maintaining high system efficiency (lower false positives, better classification). By engaging in so, this development provides an effective way of strengthening security within smart home networks, countering DDoS attacks.