Design of Neural Networks for Intrusion Detection
Abstract: There are increasing
demands for accessing information over the Internet, more and more networks are
designed and deployed. The information and network security becomes a key issue
for us to study. Neural network is effective to detect network intrusion. Much
effort has been taken in this field. Stolfo et al put forward 41 higher-level
derived features to distinguish normal connections from abnormal connections.
Unfortunately, with these 41 derived features as inputs, IDS systems take long
time to converge when training and work slowly during on-line detections. We
quantize derived features to digital type before feeding them to IDS systems.
We reduce the number of inputs while keeping IDS systems high detection rates.
After a long time of hard work, we achieved a good architecture, i.e. 18-35-1,
of BP neural network for IDS systems. And we choose trainbfg as training
function.
Author: Huiran Wang, Ruifang
Ma
Journal Code: jptkomputergg160125
