An Efficient and Effective Approach for Flooding Attack Detection in Optical Burst Switching Networks

Table 8

Accuracy and number of features of the proposed BHP flooding attack detection approaches using OBS network dataset.

Ref.	Year	Approach	# Of features	Accuracy (%)
[34]	2018	Naïve Bayes	21	79
[21]	2018	Features selection using chi-square testing (CHI) + decision tree for classification	7	87
[34]	2018	Support vector machine	21	88
[34]	2018	K-nearest neighbor	21	93
[27]	2018	Repeated incremental pruning to produce error reduction (RIPPER) rule induction algorithm	21	98
[4]	2019	Features selection using Pearson correlation coefficient (PCC) + semisupervised machine learning with k-mean	8	95.6
[34]	2018	Deep convolutional neural network (DCNN)	21	99
This work	2020	Features selection using information gain (IG) + decision tree for classification	3	100