Table 1 An overview of the contributions

Zekri M et.al [1]

Usage of C.4.5 algorithm and Signature detection algorithms were developed for automatically and successfully detecting signature attacks for DDoS attacks

Machine learning technologies such as logistic regression and naive Bayes are also used to detect intrusion

Xiaoyong Yuan et. Al [2]

A DDoS assault detection strategy based on deep learning was proposed and then compared to a regular machine learning model

To detect DDoS attacks, a mathematical model and a machine learning model were suggested, and the miss rate for each was then compared

Ceron J et.al [23]

Botnet assaults were detected using the UNBS-NB 15 and KDD99 publicity datasets

Botnet assaults were detected using CAIDA 2007 records

Mohamed Idhammad et.al [13]

An interactive, progressive, semi-supervised ML algorithm for DDoS attack detection was created using connectivity diversity prediction, co-clustering, mutual information proportion, and the Extra-Trees methodology

To identify patterns, the dataset was partitioned into 70:20:10 train, test, and validation categories, and ML strategies, notably logistic regression and naive bayes, were implemented

Saikat Das et.al [21]

NSL-KDD dataset was tested and analysed

CAIDA 2007 dataset was tested and analysed

Qian Li et.al [22]

For the real dataset, PCA-RNN was examined and shown to have significant performance increases in terms of accuracy, sensitivity, precision, and F-score

On a real-world dataset, naive bayes and logistic regression were reported to have positive performance improvements in terms of accuracy, sensitivity, precision, and F-score

Tong Anh Tuan et.al [24]

Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT), and semi-supervised learning have been used to evaluate and compare the UNBS-NB 15 dataset (USML)

The CAIDA 2007 dataset was analysed and compared to logistic regression, naive bayes, and experimented findings, with the results for both being summarised together