As the number of connected devices grows, the internet of things (IoT) poses new security challenges for network activity monitoring. Due to a lack of security understanding on the side of device producers and end users, the majority of internet of things devices are vulnerable. As a result, virus writers have found them to be great targets for converting them into bots and using them to perform large-scale attacks against a variety of targets. The authors provide deep learning models based on deep reinforcement learning for cyber security in IoT networks in this chapter. The IoT is a potential network that connects both living and nonliving things all around the world. As the popularity of IoT grows, cyber security remains a shortcoming, rendering it exposed to a variety of cyber-attacks. It should be emphasized, however, that while there are numerous DL algorithms presently, the scientific literature does not yet include a comprehensive catalogue of them. This chapter provides a complete list of DL algorithms as well as their many application areas.
Authors: Dankan Gowda, B. S. Puneeth Kumar, Ravi Shekhar, Pankaj Dadheech, N. Thangadurai
DOI: https://doi.org/10.4018/978-1-6684-4558-7.ch004
Publish Year: 2022
