Random Forest-Based Intrusion Detection System (IDS)
Random forest algorithm is used for outlier detection of network patterns in this thesis to reduce the false positive rate and improve the performance of intrusion detection systems which will help to prevent and monitor different types of attack.
Abstract
Intrusion Detection system plays an important role in network security because existing security technology is un-realistic. Most of the intrusion system (IDSs) are unable to detect intrusions due to rule based system. In this thesis random forest algorithm is used for outlier detection of network patterns. There are three intrusion techniques for intrusion detection: misuse detection , anomaly detection and hybrid detection .In this thesis the AWID-cls-R data set is used for classification. Here the aim is to reduce the false positive rate and improve the performance of intrusion detection systems which will help to prevent and monitor different types of attack.