Um Sistema de Detecção de Intrusão Baseado em Aprendizagem por Reforço

• Roger R. dos Santos PUCPR
• Eduardo K. Viegas PUCPR
• Altair O. Santin PUCPR
• Jackson Mallmann PUCPR / IFC

DOI: https://doi.org/10.5753/sbseg.2020.19252

Abstract

Over the last years, several techniques were proposed for network-based intrusion detection. However, despite the promising results reported, these techniques do not deal with changes in network traffic over time. In this paper, an approach based on reinforcement learning technique and assessment of the reliability of classifications is proposed to maintain the accuracy of the system over time. With this technique we seek to build models capable of maintaining accuracy for longer periods and assessing reliability while maintains accuracy even with outdated models. Experiments performed in a year of network traffic, showed that the proposed approach is capable of maintaining accuracy for 8 months and reliability for the evaluated period.

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