Abstract
The recent decade has seen enormous growth in the Internet of Things field. This development has significantly expanded the space for cyber-threats, among which the Distributed Denial of Service attacks have become one of the most important and common threats. These attacks might severely disrupt critical services if not detected and handled on time. To provide a reliable and secure IoT environment, accurate and effective mechanisms for detecting DDoS attacks in real-time are the most required. While state-of-the-art deep learning models like CNNs and LSTMs offer high accuracy, their computational overhead often makes them unsuitable for resource-constrained IoT environments. To address this gap, we have proposed a robust hybrid framework, the PSO-DT-based BagDT ensemble model. This model utilizes Particle Swarm Optimization in combination with Decision Tree for effectively finding the best feature subset. This lowers the dimension by reducing complexity. The proposed PSO-DT feature selection algorithm is evaluated across variants of ensemble learners, namely Random Subspace KNN, AdaBoost, RUSBoost, and Bagged Decision Trees. The PSO-DT helps in reducing the computational cost and the model size. Our PSO-DT based Bagged DT model demonstrates superior performance, achieving an accuracy of 99.96 % along with a macro-average precision, recall, and F1-score of 0.99. Among all the variants, BagDT performed better with an increase in accuracy by 4.13% and a reduction in training time by 95.49%. The overall throughput is increased by 63.52% thereby confirming the efficiency of the proposed PSO-DT-based BagDT Ensemble model for providing a real-time, scalable solution that is appropriate for implementation in contemporary smart environments.