Abstract
Traditional network-based intrusion detection systems using deep packet inspection are not feasible for modern high-speed networks due to slow processing and inability to read encrypted packet content. As an alternative to packet-based intrusion detection, researchers have focused on flow-based intrusion detection techniques. Flow-based intrusion detection systems analyze IP flow records for attack detection. IP flow records contain summarized traffic information. However, flow data is very large in high-speed networks and cannot be processed in real-time by the intrusion detection system. In this paper, an efficient multi-stage model for intrusion detection using IP flows records is proposed. The first stage in the model classifies the traffic as normal or malicious. The malicious flows are further analyzed by a second stage. The second stage associates an attack type with malicious IP flows. The proposed multi-stage model is efficient because the majority of IP flows are discarded in the first stage and only malicious flows are examined in detail. We also describe the implementation of our model using machine learning techniques.