Table 1 Summary of research analysis.

Hayder et al., 2024²⁴

To enhance the security of IoT systems by detecting proactive DDoS attacks

Combining Random forest and principal component analysis approach to reduce dimensionality issues while classifying abnormal activities

The BOT-IoT dataset is utilized for implementing this study and ensures above 95% accuracy

Facing difficulties in real-time data analysis and zero-attack interpretations

Animesh et al., 2024²⁵

To develop DDoS attack detection systems to reduce intermediate activities and counter adversarial activities

Hybridizing ensemble techniques such as Random Forest and XGBoost to improve DDoS detection accuracy

The robust dataset is utilized to train the system and ensures above 95% accuracy

System facing scalability and other attack issues while sharing information

Hani et al., 2024²⁶

To analyze software-defined networks for identifying DDoS attacks for improved security

Hybridized deep learning networks such as Convolution Networks, Gated Recurrent Networks, and Deep Learning approaches

Real-world and synthetic datasets are used for analyzing system efficiency; the hybridized approach attains above 90% accuracy

Requires additional security mechanisms to improve overall security factors in SDN

Lotfi, Mhamdi, et al., 2020²⁷

To improve the DDoS detection rate in SDN to ensure data security

Stacked Autoencoder and One-Class Support Vector Machine for identifying DDoS

The CICIDS2017 dataset is utilized for evaluating system efficiency, ensuring above 95% accuracy

Requires lightweight security mechanisms to improve other attacks in SDN

Lotfi, Mhamdi, et al., 2020²⁸

Enhance DDoS attack detection accuracy and system security in IoT

Integrated Deep Learning and Convolutional Neural Networks to improve DDoS detection accuracy

BoTNet dataset is utilized; the system ensures above 92% accuracy

High false positive rate and requires continuous evolution to minimize DDoS attacks