Intrusion detection using neural network committee machine

Abstract

Intrusion detection plays an important role in todays computer and communication technology. As such it is very important to design time efficient Intrusion Detection System (IDS) low in both, False Positive Rate (FPR) and False Negative Rate (FNR), but high in attack detection precision. To achieve that, this paper proposes Neural Network Committee Machine (NNCM) IDS. NNCM IDS consists of Input Reduction System based on Principal Component Analysis (PCA) and Intrusion Detection System, which is represented by three levels committee machine, each based on Back-Propagation Neural Network. To reduce the FNR, the system uses offline System Update, which retrains the networks when new attacks are introduced. The system shows the overall attack detection success of 99.8%.