Title: A novel deep learning model for detection of denial of service attacks in HTTP traffic over internet

Authors: V. Punitha; C. Mala; Narendran Rajagopalan

Addresses: Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India ' Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India ' Department of Computer Science and Engineering, National Institute of Technology, Puducherry, India

Abstract: The technological advancements in internet and mobile communications bring new dimension to the usage of internet applications and services. The accessibility to the enhanced services is intentionally blocked by the denial of service attacks. This paper proposes a novel deep learning classification model to detect the denial of service attacks in application layer for different network environments, such as wired network, ad hoc network and mobile ad hoc network. The simulation results illustrate that the performance of the proposed deep learning model is proficiently improved compared to existing bio-inspired and machine learning models in terms of detection accuracy and classification metrics.

Keywords: network traffic classification; denial of service attack; application layer DoS attack; slow rate DoS attacks; deep learning technique.

DOI: 10.1504/IJAHUC.2020.106666

International Journal of Ad Hoc and Ubiquitous Computing, 2020 Vol.33 No.4, pp.240 - 256

Received: 14 Feb 2019
Accepted: 05 Aug 2019
Published online: 16 Apr 2020 *

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