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Traffic Analysis of UAV Networks Using Enhanced Deep Feed Forward Neural Networks (EDFFNN)

Traffic Analysis of UAV Networks Using Enhanced Deep Feed Forward Neural Networks (EDFFNN)
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Author(s): Vanitha N. (Avinashilingam Institute for Home Science and Higher Education for Women, India & Dr. N. G. P. Arts and Science College, India) and Padmavathi Ganapathi (Avinashilingam Institute for Home Science and Higher Education for Women, India)
Copyright: 2020
Pages: 26
Source title: Handbook of Research on Machine and Deep Learning Applications for Cyber Security
Source Author(s)/Editor(s): Padmavathi Ganapathi (Avinashilingam Institute for Home Science and Higher Education for Women, India) and D. Shanmugapriya (Avinashilingam Institute for Home Science and Higher Education for Women, India)
DOI: 10.4018/978-1-5225-9611-0.ch011

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Abstract

The world is moving to an autonomous era. Autonomous vehicles take a major role in day-to-day activity, helping human personnel do work quickly and independently. Unmanned aerial vehicles (UAVs) are autonomous vehicles controlled using remotes in ground station by human personnel. These UAVs act as a network that plays a vital role in the digital era. There are different architectures of UAV networks available. This chapter concentrates on centralized UAV network. Because of wireless and autonomy characteristics, these networks are prone to various security issues, so it's very important to monitor and analyze the traffic of the UAV network in order to identify the intrusions. This chapter proposes enhanced deep feed forward neural network (EDFFNN) in order to monitor and analyze the traffic of the UAV network to detect the intrusions with maximum detection rate of 94.4%. The results have been compared with the previous method of intrusion detection.

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