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A Deep Learning Approach for Detection of Application Layer Attacks in Internet
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Author(s): V. Punitha (National Institute of Technology, Tiruchirappalli, India)and C. Mala (National Institute of Technology, Tiruchirappalli, India)
Copyright: 2020
Pages: 14
Source title:
Handling Priority Inversion in Time-Constrained Distributed Databases
Source Author(s)/Editor(s): Udai Shanker (Madan Mohan Malaviya University of Technology, India)and Sarvesh Pandey (Madan Mohan Malaviya University of Technology, India)
DOI: 10.4018/978-1-7998-2491-6.ch010
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Abstract
The recent technological transformation in application deployment, with the enriched availability of applications, induces the attackers to shift the target of the attack to the services provided by the application layer. Application layer DoS or DDoS attacks are launched only after establishing the connection to the server. They are stealthier than network or transport layer attacks. The existing defence mechanisms are unproductive in detecting application layer DoS or DDoS attacks. Hence, this chapter proposes a novel deep learning classification model using an autoencoder to detect application layer DDoS attacks by measuring the deviations in the incoming network traffic. The experimental results show that the proposed deep autoencoder model detects application layer attacks in HTTP traffic more proficiently than existing machine learning models.
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