Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Probabilistic similarity networks
Probabilistic similarity networks
Internetworking with TCP/IP (2nd ed.), vol. I
Internetworking with TCP/IP (2nd ed.), vol. I
Interconnections: bridges and routers
Interconnections: bridges and routers
Protecting routing infrastructures from denial of service using cooperative intrusion detection
NSPW '97 Proceedings of the 1997 workshop on New security paradigms
On the implementation of a prototype for performance management services
ISCC '95 Proceedings of the IEEE Symposium on Computers and Communications (ISCC'95)
Bayesian classification with correlation and inheritance
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Modeling adaptive autonomous agents
Artificial Life
Using self-organizing networks for intrusion detection
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
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Intrusion Detection in large network must rely on use of many distributed agents instead to one large monolithic module. Agents should have some kind of artificial intelligence in order to cope successfully with different intrusion problems. In this paper, we suggested Bayesian alarm network to work as independent Network Intrusion Detection Agent. We have shown that when narrowed in detecting one specific type of the attack in large network, for example denial of service, virus, worm or privacy attack, we can induce much more prior knowledge into system regarding the attack. Different nodes of the network can develop their own model of Bayesian alarm network and agents could communicate between themselves and with common security data base. Networks should be organized hierarchically so on the higher level of hierarchy, Bayesian alarm network, thanks to interconnections with lower level networks and data, acts as a distributed Intrusion Detection System.