An ensemble of LSTM, GRU, and stacked autoencoders trained only on normal web requests is reported to detect zero-day web attacks with 97.58 percent accuracy and a 0.2 percent false-positive rate on CSIC2012.
Machine learning for web vulnerability detection: the case of cross-site request forgery
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Detecting Zero-Day Web Attacks with an Ensemble of LSTM, GRU, and Stacked Autoencoders
An ensemble of LSTM, GRU, and stacked autoencoders trained only on normal web requests is reported to detect zero-day web attacks with 97.58 percent accuracy and a 0.2 percent false-positive rate on CSIC2012.