A performance evaluation of deep reinforcement learning for model-based intrusion response
| Autor: | Valeria Cardellini, Stefano Iannucci, Ioana Banicescu, Ovidiu Daniel Barba |
|---|---|
| Přispěvatelé: | Stefano Iannucci, Ovidiu Daniel Barba, Valeria Cardellini, Ioana Banicescu, Iannucci, S., Barba, O. D., Cardellini, V., Banicescu, I. |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Computation Intrusion response systems Limiting Machine learning computer.software_genre Intrusion Response Orders of magnitude (bit rate) Self Protecting Systems Stateful firewall Reinforcement learning Artificial intelligence Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni business computer Curse of dimensionality Intrusion response |
| Zdroj: | FAS*W@SASO/ICAC |
| Popis: | Given the always increasing size of computer systems, manually protecting them in case of attacks is infeasible and error-prone. For this reason, several Intrusion Response Systems (IRSs) have been proposed so far, with the purpose of limiting the amount of work of an administrator. However, since the most advanced IRSs adopt a stateful approach, they are subject to what Bellman defined as the curse of dimensionality. In this paper, we propose an approach based on deep reinforcement learning which, to the best of our knowledge, has never been used until now for intrusion response. Experimental results show that the proposed approach reduces the time needed for the computation of defense policies by orders of magnitude, while providing near-optimal rewards. |
| Databáze: | OpenAIRE |
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