A Fast and Effective Method for Intrusion Detection using Multi-Layered Deep Learning Networks

The practise of recognising unauthorised abnormal actions on computer systems is referred to as intrusion detection. primary goal an Intrusion Detection System (IDS) identify user behaviours normal or based the data they communicate. Firewalls, encryption, and authentication techniques were all employed in traditional security systems. Current scenarios, other hand, are very complex capable readily breaching measures provided by previous protection However, current scenarios highly sophisticated easily breaking mechanisms imposed Detecting intrusions a challenging aspect especially networked environments, system designed for such scenario should be able handle huge volume velocity associated with domain. This research presents three models, APID (Adaptive Parallelized Detection), HBM (Heterogeneous Bagging Model) MLDN (Multi Layered Deep learning Network) that can used fast efficient detection environments. deep model has been constructed using Keras library. training preprocessed segregated fit processing architecture neural networks. network multiple layers required parameters set accordance input data. trained validated validation specifically this purpose.