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Table 1 An overview of the contributions

From: Detecting Denial of Service attacks using machine learning algorithms

Authors Their contributions Proposed work
Zekri M [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 [23] Botnet assaults were detected using the UNBS-NB 15 and KDD99 publicity datasets Botnet assaults were detected using CAIDA 2007 records
Mohamed Idhammad [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 [21] NSL-KDD dataset was tested and analysed CAIDA 2007 dataset was tested and analysed
Qian Li [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 [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