TY  - EJOU
AU  - Albalawi, Sultan Shutyan 
AU  - Idris, Mohd Yamani Idna 
AU  - Wahab, Ainuddin Wahid Bin Abdul 

TI  - TS-SCHO–TSE: A Hybrid Optimization Framework for Feature Selection and Ensemble Learning in DDoS Detection
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - As Distributed Denial-of-Service (DDoS) attacks grow in size and complexity, standard intrusion detection systems are hitting a wall. Most struggle to generalize well, require too much computational power, or lack model diversity. To tackle this, we developed TS-SCHO-TSE, a unified hybrid framework for DDoS detection. Unlike traditional methods that treat feature selection and ensemble building as isolated, step-by-step tasks, our approach combines everything. We map feature masks, classifier states, voting weights, and hyperparameters into a single mixed discrete-continuous search space to optimize them all at once. We tested our system against classical functions (F1–F23), the CEC2019 benchmark suite, and three concrete network datasets: public traffic from CICIDS2017 and CICDDoS2019, plus a real-world dataset we captured using Wireshark. The results show that TS-SCHO-TSE outperforms existing methods, hitting 99.58% accuracy on CICIDS2017, 98.94% on CICDDoS2019, and 98.47% on our captured data. Crucially, the framework thins out the feature space by 46%—dropping from 41 features down to 22—and cuts inference latency to just 6.74 ms per 1000 flows. This proves the framework serves as a fast, highly accurate, and scalable alternative to heavy deep-learning models in live security environments.
KW  - DDoS detection; intrusion detection systems; feature selection; metaheuristic optimization; TS-SCHO; ensemble learning

DO  - 10.32604/cmc.2026.083860