Network Security in the Age of Reinforcement Learning

Authors

  • Mohit Dayal Ambedkar Institute of Advanced Communication Technologies & Research New Delhi, India
  • Rupender Duggal Ambedkar Institute of Advanced Communication Technologies & Research New Delhi, India
  • Siddharth Aggarwal Maharaja Agrasen Institute of Technology, New Delhi, India

Keywords:

Reinforcement Learning, Network Security

Abstract

In the internet age, networks are everywhere. The ability to connect on a call, to browse the web, to push your content for viewers online, to send someone a message and to do a billion of other things comes from the availability of networks. As the networks have diversified and have become omnipresent, they have also become vulnerable and exposed. Thus, network security has become a huge priority for commercial and personal users. The concern of data leakage or stealing is not only a question of privacy but many also incur loss of money, property and sometimes even identity. Fortunately, along with the growth of computer networks, computational methods and practices such as machine learning, cryptography, block-chain etc. have also evolved and have become more efficient and applicable to solve real world problems. One such area of computer science, that is just breaking out of its shell is Reinforcement Learning. Reinforcement Learning is a field within Machine Learning which aims to make machine make intelligent and planned decisions. This paper discusses some of the recent works done in the field of network security using Deep Reinforcement Learning.

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Published

2020-08-14

How to Cite

Dayal, M., Duggal, R., & Aggarwal, S. (2020). Network Security in the Age of Reinforcement Learning. International Journal of Recent Advances in Science and Technology, 7(2). Retrieved from https://www.ijrast.com/index.php/ijrast/article/view/53