Model-Free Deep Reinforcement Learning in Software-Defined Networks

TL;DR

This paper compares two deep reinforcement learning algorithms for cybersecurity in software-defined networks, analyzing their performance through game-based simulations and statistical tests to determine their relative effectiveness.

Contribution

It provides a comparative analysis of Neural Episodic Control and Double Deep Q-Networks in a cybersecurity context within SDN environments, highlighting their similar performance.

Findings

No significant difference in game performance between the two algorithms

Both algorithms perform similarly in cybersecurity game scenarios

Statistical analysis shows comparable effectiveness of the approaches

Abstract

This paper compares two deep reinforcement learning approaches for cyber security in software defined networking. Neural Episodic Control to Deep Q-Network has been implemented and compared with that of Double Deep Q-Networks. The two algorithms are implemented in a format similar to that of a zero-sum game. A two-tailed T-test analysis is done on the two game results containing the amount of turns taken for the defender to win. Another comparison is done on the game scores of the agents in the respective games. The analysis is done to determine which algorithm is the best in game performer and whether there is a significant difference between them, demonstrating if one would have greater preference over the other. It was found that there is no significant statistical difference between the two approaches.