Enhancing DDoS Attack Detection in IoT Networks Through Pruned Deep Ensemble Learning

DDoS attacks prove to be the biggest roadblocks to the maintenance and stability of the IoT environment, occasionally causing network congestions, shutdowns, and breaches of data integrity. In this regard, setting up an intelligent ensemble learning framework for DDoS detection over IoT infrastructures involves pruning of models with an aim of maximizing performance and minimizing computation. The ensemble learning consists of two models first, stacking classifier RF , GB and second, voting classifier of LR, DT, KNN, CNN and LSTM. TPOT Classifier is an automatic approach to model and hyperparameter selection and tuning, maintaining optimized ML pipelines. The features of the dataset include some major network flow feature attributes such as time, the total amount of forward directions of packets, the total amount of backward directions of packets, The packet length forward, the packet length backward, packets backward, statistical length of packet, and the amount of the packet flag. This is very wide scope of factors which is necessary to define the valid traffic. The tendency of pruning ( redundancy or less informative ) of the ensemble models provides an opportunity of removing the redundant or less informative classifiers, improve computing efficiency without reducing the predictive accuracy. As to the existing measures of performance, The CNN, LSTM outcomes of the experiment that have been described in the paper have shown that the given technique might be effectively employed in an effort to target the identification of DDoS attacks, and the false positives rates would not be high. The high prevalence of DDoS attacks makes the proposed methodology worthwhile in terms of detecting DDoS due to the scalability of the method as well as because IoT networks are limited in resources. The experimental results reflect that fine ensemble training with pruning can show a superior trade-off relative to the traditional machine learning methods; therefore, providing a mature and scaled security methodology in IoT network.