Mobile Target Defence Against IoT-DDoS Attacks

This study analyses the mobile target defence method and feature extraction process based on multi-source information fusion technology (MSIFT), and introduces a feature level fusion (FLF) method for optimising backpropagation neural network (BPNN) DDoS attacks based on genetic algorithm. The models with 9 nodes and 11 nodes had the best learning performance, with learning rates of 0.37 and 0.15. When the intensity of DDoS attacks was low, the prediction accuracy of the proposed method was about 94%. The actual value was usually small, with the 10th group having the highest actual value, close to 800, and the 19th group having the lowest actual value, about 130. Introducing decision level fusion of DDoS attacks based on D-S evidence fusion can further improve the accuracy of attack detection. This study has made significant progress in improving the efficiency and accuracy of mobile target defence against DDoS attacks in the Internet of Things.