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Review of Reinforcement Learning Techniques

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Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 31)

Abstract

The paper with the help of reinforcement learning techniques and its method helps to find the best techniques that can be used in cyber security to help defender protect the data against the attackers. The techniques have been used in a cyber security game and resulted in a game of an unfriendly consecutive decision making problem played between agents i.e. an attacker and a defender.

Keywords

  • Cyber security game
  • Network
  • Agents
  • Standard network
  • Game procedure
  • Reinforcement learning
  • Neural Network

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  • DOI: 10.1007/978-3-030-24643-3_108
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Fig. 1.

References

  1. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multi-armed bandit problem. Ma-chine Learn. 47, 235–256 (2002)

    CrossRef  Google Scholar 

  2. Chung, K., Kamhoua, C., Kwiat, K., Kalbarczyk, Z., Iyer, K.: Game theory with learning for cyber security monitoring. IEEE HASE, pp. 1–8 (2016)

    Google Scholar 

  3. Neumann, J.V., Morgenstern, O.: Theory of games and economic behavior. Princeton University Press (2007)

    Google Scholar 

  4. Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. The MIT press, Cambridge, MA (1998)

    MATH  Google Scholar 

  5. Garivier, A., Moulines, E.: On upper-confidence bound policies for non-stationary bandit problems. ALT (2008)

    Google Scholar 

  6. Wang, Y., Li, T., Lin, C.: Backward q-learning: the combination of sarsa algorithm and q-learning. Eng. Appl. of AI 26, 2184–2193 (2013)

    CrossRef  Google Scholar 

  7. Lin, L.-J.: Reinforcement learning for robots us-ing neural networks. PhD thesis, Carnegie Mellon University (1993)

    Google Scholar 

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Correspondence to Mohit Malpani .

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Malpani, M., Mathew, R. (2020). Review of Reinforcement Learning Techniques. In: Pandian, A., Senjyu, T., Islam, S., Wang, H. (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018). ICCBI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 31. Springer, Cham. https://doi.org/10.1007/978-3-030-24643-3_108

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  • DOI: https://doi.org/10.1007/978-3-030-24643-3_108

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24642-6

  • Online ISBN: 978-3-030-24643-3

  • eBook Packages: EngineeringEngineering (R0)