id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_si36pup6v5frdgnsep24cnugs4	Fawaz Mahiuob Mohammed Mokbal	Data augmentation-based conditional Wasserstein generative adversarial network-gradient penalty for XSS attack detection system	2020	20	.pdf	application/pdf	8744	1459	67	samples of minority class that have identical distribution as real XSS attack scenarios. Data augmentation-based conditional Wasserstein generative adversarial network-gradient penalty for XSS attack detection system. (attack labels) to generate valid and indistinguishable samples of real XSS attack data. real training data arbitrarily; the process is performed only if the generated sample x̃ • The proposed method is evaluated with two real and large unbalance XSS attack datasets. generative model G for learning the distribution of data and, second, the discriminator D, generate synthetic samples of attack class (minority) with identical distribution to real XSS models using real training dataset to generate synthetic data. generate any sample found in the XSS attack training dataset. Table 3 Detection results using data augmented generated through different methods on the CICIDS2017 dataset. Table 4 Detection results using data augmented generated through different methods on the second dataset.	./cache/work_si36pup6v5frdgnsep24cnugs4.pdf	./txt/work_si36pup6v5frdgnsep24cnugs4.txt