The Conditional Random Fields of Layered Approach using Intrusion Detection
Kolikipogu Ramakrishna · 2012
Intrusion detection faces a number of challenges; 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. 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 implementing the Layered Approach. Experimental results on the benchmark KDD’99 intrusion data set show that our proposal system based on Layered conditional Random fields outperforms other well-known methods such as the decision trees and the naive Bayes. the improvement in attack detection accuracy is very high, particularly, for the U2R attacks and the R2L attacks. Statistical Tests also demonstrate higher confidence in detection accuracy for our method. Finally, we show that our system is robust and is able to handle noisy data without compromising performance.