Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection

Table 1

The symbols are used in the formulas from (1) to (15) in Section 3.3.

No.	Math symbols	Meaning of the math symbols
1		The training set
2		The size of the training set
3		The majority class of the training set
4		The size of the majority class in the training set
5		The minority class of the training set
6		The size of the minority class in the training set
7		The imbalance ratio of the imbalanced data sets
8		The oversampling rate of the minority class
9		The false data generated by the generator of GAN
10		The output data of the generator
11		The output data of the discriminator
12		The set of all possible joint distributions of the combination of and
13		One joint distribution of all possible joint distributions of the combination of and
14		The function of neural network model
15		The Lipschitz constant
16		The Wasserstein distance
17	,	The iterations in the training process of CWGAN
18	,	The training epoch in each iteration
19		The training epoch in iteration
20		The loss function of the discriminator
21		The training stride in the training process of CWGAN
22		The optimal solution of
23		The optimal value of
24		The Wasserstein distance between generated data and real data when the discriminator is convergent at the -th iteration.