Enhancing efficiency of intrusion prediction based on intelligent immune method
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Typed linear chain conditional random fields and their application to intrusion detection
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Layered approach for intrusion detection using naïve Bayes classifier
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
A-GHSOM: An adaptive growing hierarchical self organizing map for network anomaly detection
Journal of Parallel and Distributed Computing
Proceedings of the Fifth International Conference on Security of Information and Networks
Minimal complexity attack classification intrusion detection system
Applied Soft Computing
Fuzzy particle swarm optimization for intrusion detection
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
ACTIDS: an active strategy for detecting and localizing network attacks
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
Context and semantics for detection of cyber attacks
International Journal of Information and Computer Security
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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. 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 implementing the Layered Approach. Experimental results on the benchmark KDD '99 intrusion data set show that our proposed 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 (34.8 percent improvement) and the R2L attacks (34.5 percent improvement). 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.