Implementation of Layered CRF's For Intrusion Detection

ABSTRACT Intrusion detection faces a number of challenges; an intrusion detection system must reliably detect mali-cious activities in a network and must perform efficiently to cope with the large amount of network traffic. In this paper, we address these two issues of Accuracy and Efficiency using Conditional Random Fields and Layered Approach. We demonstrate that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by imple-menting the Layered Approach. Intrusion detection is one of the high priority and challenging tasks for network administrators and security professionals. More sophisticated security tools mean that the attackers come up with newer and more advanced penetration methods to defeat the installed security systems. Finally, our system has the advantage that the number of layers can be increased or decreased depending upon the environment in which the system is deployed, giving flexibility to the network administrators. The areas for future research include the use of our method for extracting features that can aid in the development of signatures for signature-based systems. The signature-based systems can be deployed at the periphery of a network to filter out attacks that are frequent and previously known, leaving the detection of new unknown attacks for anomaly and hybrid systems