Sparse autoencoder + principal component + light gradient boosted machine
ISCX-URL
Sparse autoencoder + principal component are applied for feature learning; light gradient boosted machine is used for feature selection and classification
Concatenation of Word2vec and TF-IDF is used for feature vectors. M-ResNet is applied for feature discrimination purpose. Classification is performed using a FastText classifier
Autoencoders, restricted Boltzmann machine, deep belief networks, CNN, and more
The datasets used in the literature are covered
Review of application of deep learning approaches. It covers feature representation, model training, model robustness enhancement techniques, and problems and challenges of developments
Concatenated form of SAE and DAE outputs is fed into a GAN for feature representation. The deep Boltzmann machine is used to identify attacks. For identifying the different types of attacks, Bi-LSTM is used
DRN: the percentage of all normal requests that are classified as normal; MSE: mean square error; MAD: mean absolute deviation; PMAD: percent mean absolute deviation; MAPE: mean absolute percentage error. Acc, Prec, Rcll, , and FPR: accuracy, precision, recall, score, and false-positive rate, respectively. The overall performance of relative datasets is given. In studies containing multiple dataset evaluations, the provided performance is related to the bold dataset.