基于黑板的自治代理协同入侵检测系统模型

人侵检测系统是针对网络攻击的重要检测机制。迅速发展的网络技术、互联网流量的迅速增长以及攻击的复杂性使得现存的人侵检测系统难以提供可靠的服务。这就需要考虑优化性能，提供一种具备学习和自适应能力的入侵检测系统。文章提出了一种入侵检测模型，它基于自治agent和黑板结构，具有更好的自学习能力，这种检测系统能够自动适应网络环境和攻击类型的不断变化提高自身对新型的网络攻击的检测能力。

Model of Learning Intrusion Detection System based on Autonomous Agent

The Intrusion detection system of attacks against computer networks is a type of important detection mechanism in the field of network security.The dexterity of the attackers,the developing technologies and the enormous growth of internet traffic have made it difficult for any existing intrusion detection system to offer a reliable service.There is a requirement for a system with learning and adapting capabilities for optimal performance.This paper issues the model of a learning intrusion detection system including a blackboard-based architecture based on autonomous agents.It has the capability for online learning,which may result in better performance than present systems.This feature enables the system to adapt to changes in the network environment as it assimilates more network data.