Classic Web Application Firewalls (WAFs) mostly use rule-based approach for attack detection. This approach is known to have its pros and cons. Despite offering decent protection from automated attacks and predictable detection results rule-based approach has and always will have certain disadvantages. We all know that it’s useless against 0-day attacks or that even the most sophisticated rules are easily evaded by skilled professionals. That is why a more effective approach should involve some kind of heuristics. Let’s give a chance to artificial intelligence to find something non-obvious for human perception in raw data and try to explain its results. To this day AI has been more often used for cat classification rather than for detecting application-level attacks on HTTP applications. Our team decided to test the hypothesis that Deep Learning is able to detect web-based attacks effectively. We started with very simple neural network architectures and tried to use them for classification. After some experiments it became clear that we needed more complex networks so we abandoned our attempts to use classification shifting to anomaly detection. Eventually, we ended up using seq2seq model with attention mechanisms which is able to detect zero-day web attacks with minimal number of false positives.