Divided two-part adaptive intrusion detection system

Abstract

The main objective of this paper is to design a more complete intrusion detection system solution. The paper presents an efficient approach for reducing the rate of alerts using divided two-part adaptive intrusion detection system (DTPAIDS). The proposed DTPAIDS has a high degree of autonomy in tracking suspicious activity and detecting positive intrusions. The proposed DTPAIDS is designed with the aim of reducing the rate of detected false positive intrusion through two achievements. The first achievement is done by implementing adaptive self-learning neural network in the proposed DTPAIDS to gives it the ability to be automatic adaptively system based on Radial Basis Functions (RBF) neural network. The second achievement is done through dividing the proposed intrusion detection system IDS into two parts. The first part is IDS1, which is installed in the front of firewall and responsible for checking each entry user's packet and deciding if the packet considered is an attack or not. The second is IDS2, which is installed behind the firewall and responsible for detecting only the attacks which passed the firewall. This proposed approach for IDS exhibits a lower false alarm rate when detects novel attacks. The simulation tests are conducted using DARPA 1998 dataset. The experimental results show that the proposed DTPAIDS [1] reduce false positive rate, [2] detects intrusion occurrence sensitively and precisely, [3] accurately self---adapts diagnoser model, thus improving its detection accuracy.