IEEE Transactions on Software Engineering - Special issue on computer security and privacy
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Prediction algorithms and confidence measures based on algorithmic randomness theory
Theoretical Computer Science - Natural computing
Transductive Confidence Machines for Pattern Recognition
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Learning nonstationary models of normal network traffic for detecting novel attacks
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Detecting outliers using transduction and statistical testing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A study in using neural networks for anomaly and misuse detection
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
An anomaly intrusion detection method using the CSI-KNN algorithm
Proceedings of the 2008 ACM symposium on Applied computing
TCM-KNN scheme for network anomaly detection using feature-based optimizations
Proceedings of the 2008 ACM symposium on Applied computing
An Experimental Study on Instance Selection Schemes for Efficient Network Anomaly Detection
RAID '09 Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection
Optimizing network anomaly detection scheme using instance selection mechanism
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A self-tuning self-optimizing approach for automated network anomaly detection systems
Proceedings of the 9th international conference on Autonomic computing
A-GHSOM: An adaptive growing hierarchical self organizing map for network anomaly detection
Journal of Parallel and Distributed Computing
| Hi-index | 0.00 |
Intrusion detection is a critical component of secure information systems. Network anomaly detection has been an active and difficult research topic in the field of Intrusion Detection for many years. However, it still has some problems unresolved. They include high false alarm rate, difficulties in obtaining exactly clean data for the modeling of normal patterns and the deterioration of detection rate because of some "noisy" data in the training set. In this paper, we propose a novel network anomaly detection method based on improved TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors) machine learning algorithm. A series of experimental results on the well-known KDD Cup 1999 dataset demonstrate it can effectively detect anomalies with high true positive rate, low false positive rate and high confidence than the state-of-the-art anomaly detection methods. In addition, even interfered by "noisy" data (unclean data), the proposed method is robust and effective. Moreover, it still retains good detection performance after employing feature selection aiming at avoiding the "curse of dimensionality".