Application Layer Denial of Service Attack Detection using Deep Learning Approach - Mahagaonkar, AB; Buchade, AR (International Journal of Computer Sciences and Engineering, 2025)
Denial of Service attack, is one of the deadliest attacks of the Internet era. It’s major objective is to prevent legitimate users from accessing services over a network. DoS attacks can be broadly classified into network layer and application layer attacks. In this paper focus is on detection of well-known HTTP based application layer DoS attacks. We have proposed an integrated solution for detection of both volumetric and non-volumetric HTTP based application layer DoS attacks. The proposed system uses an in-memory analytics mechanism to extract the input feature set from the live traffic. On the basis of its learning from the training phase the deep neural network identifies the attacker using the feature set. We have used the TensorFlow to build the deep neural network. We have built a conformation mechanism to further reduce false positives. The result reveals that the proposed system can achieve 99.92% classification accuracy with only 0.003% false positives.