Applying Rough Sets and BP Neural Networks to Detect Network Intrusions
Instruction detection technology, which makes up for the shortages of firewall and data security protection, is an important part in Policy Protection Detection Response model. However, network intrusions detection involves many influencing factors and is an online process which requires quick and accurate technologies. The paper combines rough sets theory and BP neural networks to detect the abnormal intrusions in local area networks. The introduction of rough sets cuts down the input dimensions of BP neural networks, and the BPNN algorithm is improved by adding the momentum factor mc and applying adaptive learning rate.