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Robust Intrusion Detection System using Layered Approach with Conditional Random Fields

Network Security is fast becoming an absolute necessity to protect the information contained on computer systems worldwide. The ever changing network use and operation along with the public concern for protection of sensitive information makes implementing an efficient and effective security plan a must. Intrusion detection Systems faces a number of challenges, One component of this approach is that of network intrusion detection systems what we are discussing in this paper uses Snort signature database. An intrusion detection system must reliably detect malicious activities in a network and must perform efficiently to cope with the large amount of network traffic. Making a network intrusion detection system work effectively should not degrade overall network performance so some type of performance measurement plan must be designed and implemented. Intrusion Detection Systems have become very popular in the recent years. This is due to the obvious explosive growth of the Internet and the fact that most us keep and access sensitive data online.  In this paper, we have addressed these two issues of accuracy and efficiency using conditional random fields and layered approach. We have demonstrated that high attack detection accuracy can be achieved by using conditional random fields and high efficiency by implementing the layered approach. Finally, we have resolved that this system is robust and is able to handle noisy data without compromising the network performance.