IEEE Transactions on Software Engineering - Special issue on computer security and privacy
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
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
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
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
Network anomaly detection based on TCM-KNN algorithm
ASIACCS '07 Proceedings of the 2nd ACM symposium on Information, computer and communications security
International Journal of Information Security and Privacy
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With the rapid increase of network threats and cyber attacks, network security problem is becoming more and more serious. Network anomaly detection is a key technique to secure information systems and resist cyber attacks. In this paper, we first propose an efficient network anomaly detection technique based on TCM-KNN scheme. Secondly, we emphasize the feature-based optimizations for our TCM-KNN. We employ feature selection and feature weight mechanisms to optimize TCM-KNN as a promising lightweight and on-line anomaly detection technique both in reducing its computational cost and in boosting its detection performance. A series of experiments on well-known intrusion detection dataset KDD Cup 1999 demonstrate the effectiveness of our methods presented in this paper.