Adaptation of the neural network-based IDS to new attacks detection
作者： Przemyslaw Kukielka , Zbigniew Kotulski
DOI:
关键词:
摘要: In this paper we report our experiment concerning new attacks detection by a neural network-based Intrusion Detection System. What is crucial for topic the adaptation of network that already in use to correct classification "normal traffic" and an attack representation not presented during training process. When it comes should also be easy obtain vectors test retrain classifier. We describe proposal algorithm distributed IDS architecture could achieve goals mentioned above.
arxiv.org
harvard.edu
128.84.21.199参考文章(5)
Wenke Lee, Salvatore J. Stolfo, Data mining approaches for intrusion detection usenix security symposium. pp. 6- 6 ,(1998) , 10.21236/ADA401496
Przemyslaw Kukielka, Zbigniew Kotulski, Analysis of different architectures of neural networks for application in Intrusion Detection Systems international multiconference on computer science and information technology. pp. 807- 811 ,(2008) , 10.1109/IMCSIT.2008.4747335
Richard Lippmann, Joshua W Haines, David J Fried, Jonathan Korba, Kumar Das, The 1999 DARPA off-line intrusion detection evaluation recent advances in intrusion detection. ,vol. 34, pp. 579- 595 ,(2000) , 10.1016/S1389-1286(00)00139-0
Wenke Lee, Salvatore J. Stolfo, A framework for constructing features and models for intrusion detection systems ACM Transactions on Information and System Security. ,vol. 3, pp. 227- 261 ,(2000) , 10.1145/382912.382914
Tsong Song Hwang, Tsung-Ju Lee, Yuh-Jye Lee, A three-tier IDS via data mining approach Proceedings of the 3rd annual ACM workshop on Mining network data - MineNet '07. pp. 1- 6 ,(2007) , 10.1145/1269880.1269882