基于主机的入侵检测自适应批评设计

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) Pub Date : 2002-08-07 DOI:10.1109/IJCNN.2002.1007777

T. Draelos, D. Duggan, M. Collins, D. C. Wunsch

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引用次数: 7

摘要

我们探索了基于主机的入侵检测的自适应批评设计，因为它们利用了强化学习，这允许在传感器数据中难以精确定位的学习漏洞。Solaris基本安全模块审计数据的结果展示了学习区分干净数据和利用数据的能力。

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Adaptive critic designs for host-based intrusion detection

We explore adaptive critic designs for host-based intrusion detection because of their utilization of reinforcement learning, which allows learning exploits that are difficult to pinpoint in sensor data. Results on Solaris basic security module audit data demonstrate an ability to learn to distinguish between clean and exploit data.

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Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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