Prediction with local patterns using cross-entropy
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the 'Dimensionality Curse' and the 'Self-Similarity Blessing'
IEEE Transactions on Knowledge and Data Engineering
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Theory of nearest neighbors indexability
ACM Transactions on Database Systems (TODS)
Detecting anomalous records in categorical datasets
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Angle-based outlier detection in high-dimensional data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Unusual pattern detection in high dimensions
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A nonparametric outlier detection for effectively discovering top-n outliers from engineering data
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A fast randomized method for local density-based outlier detection in high dimensional data
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
| Hi-index | 0.00 |
We introduce a feature-based method to detect unusual patterns. The property of normality allows us to devise a framework to quickly prune the normal observations. Observations that can not be combined into any significant pattern are considered unusual. Rules that are learned from the dataset are used to construct the patterns for which we compute a score function to measure the interestingness of the unusual patterns. Experiments using the KDD Cup 99 dataset show that our approach can discover most of the attack patterns. Those attacks are in the top set of unusual patterns and have a higher score than the patterns of normal connections. The experiments also show that the algorithm can run very fast.