Weighted transformer neural network for web attack detection using request URL

Web application firewalls (WAFs) and other Intrusion Detection Systems (IDS) techniques are employed to defend the network against web attacks. Even so, attacks may succeed since most WAFs demand extensive configuration expertise that depends on filters. Despite notable successes, deep information has been utilized in varied applications. Still, it’s crucial to have a reliable method for detecting the attack due to the attacker’s various ways of concealment of the URLs. Several methods were introduced for detecting the attacks in web applications; still, the accuracy of detection and the computation burden are challenging aspects. Hence, a web attack detection mechanism is introduced in this research using the deep learning framework using the URL request. The proposed method utilizes a three-fold attack detection strategy to detect the attack with minimal computation complexity. Initially, the profile is checked to determine the genuinity of a user, and then, the bot scanners are identified using the generalized adversarial network (GAN). Finally, the attack detection is employed using the transformer neural network, wherein the adjustable parameters are modified using the weighted mean of vectors (INFO) optimization technique. The performance of a proposed method is evaluated based on various assessment measures like Accuracy, Precision, Recall, F-Measure, TPR, FPR, FNR and TNR and acquired the values of 99.97%, 99.96%, 99.97%, 99.97%, 99.97%, 0.03%, 0.03%, and 99.97% respectively.