Using Active Learning in Intrusion Detection
CSFW '04 Proceedings of the 17th IEEE workshop on Computer Security Foundations
Intrusion detection using hierarchical neural networks
Pattern Recognition Letters
A blackboard-based learning intrusion detection system: a new approach
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
An Extraction Method of Situational Factors for Network Security Situational Awareness
ICICSE '08 Proceedings of the 2008 International Conference on Internet Computing in Science and Engineering
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
| Hi-index | 0.00 |
Assuring the security of networks is an increasingly challenging task. The number of online services and migration of traditional services like stocktrading and online payments to the Internet is still rising. On the other side, criminals are attracted by the values of business data, money transfers, etc. Therefore, safeguarding the network infrastructure is essential. As Intrusion Detection Systems (IDS) had been in the focus of a numerous of researches for the last years, several sophisticated solutions had been found. Very capable IDS are based on neural networks. However, these systems lack of an adaptability to dynamic changing environments or require a protracted learning phase before they are operational. The approach is to overcome these restrictions by introducing a modular neural network based on pre-processed components supplemented by static policies. By that, it is possible to overcome long-lasting learning phases.