Analysis of active intrusion prevention data for predicting hostile activity in computer networks

The wide use of the computer networks and the Internet has increased the concern for the security and protection from hostile activity. Many organizations are using additional protective measures in the form of intrusion detection systems (IDS) to monitor the activity on the network to detect the unusual, and potentially hostile activity. Intrusion prevention systems (IPS) block the attacks in real time and provide an additional layer of security, and operate online by matching network activity patterns to the signatures of known modes of attack. A new approach called Active Intrusion Prevention (AIP) is emerged that examines all the activities on the network, and provides the requested data with an early and accurate identification, prior to the actual break-in and protection from all types of attacks. AIP systems are analyzed with predictive models and logit regression analysis and then applied to enhance computer network security.