A novel deep learning model for detection of denial of service attacks in HTTP traffic over internet. (8th April 2020)

• Record Type: Journal Article
• Authors: Punitha, V.; Mala, C.; Rajagopalan, Narendran
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 33:Number 4(2020)
• Page Start: 240
• Page End: 256
• Publication Date: 2020-04-08
• Subjects: network traffic classification -- denial of service attack -- application layer DoS attack -- slow rate DoS attacks -- deep learning technique; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

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.