New Exact Concise Representation of Rare Correlated Patterns: Application to Intrusion Detection
During the last years, many works focused on the exploitation of rare patterns. In fact, these patterns allow conveying knowledge on unexpected events. Nevertheless, a main problem is related to their very high number and to the low quality of several mined rare patterns. In order to overcome these limits, we propose to integrate the correlation measure bond aiming at only mining the set of rare correlated patterns. A characterization of the resulting set is then detailed, based on the study of constraints of different natures induced by the rarity and the correlation. In addition, based on the equivalence classes associated to a closure operator dedicated to the bond measure, we propose a new exact concise representation of rare correlated patterns. We then design the new RcprMiner algorithm allowing an efficient extraction of the proposed representation. The carried out experimental studies prove the compactness rate offered by our approach. We also design an association rules based classifier and we prove its effectiveness in the context of intrusion detection.
KeywordsConcise representation Constraint Rarity Correlation Closure operator Equivalence class
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB 1994), Santiago, Chile, pp. 487–499 (1994)Google Scholar
- 2.Ben Amor, N., Benferhat, S., Elouedi, Z.: Naive bayes vs decision trees in intrusion detection systems. In: Proceedings of the ACM Symposium on Applied Computing (SAC 2004), Nicosia, Cyprus, pp. 420–424 (2004)Google Scholar
- 4.Boulicaut, J.F., Jeudy, B.: Constraint-based data mining. In: Data Mining and Knowledge Discovery Handbook, 2nd edn., pp. 339–354. Springer (2010)Google Scholar
- 5.Ganter, B., Wille, R.: Formal Concept Analysis. Springer (1999)Google Scholar
- 7.Koh, Y.S., Rountree, N.: Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection. IGI Global Publisher (2010)Google Scholar
- 14.Surana, A., Kiran, R.U., Reddy, P.K.: Selecting a right interestingness measure for rare association rules. In: Proceedings of the 16th International Conference on Management of Data (COMAD 2010), Nagpur, India, pp. 115–124 (2010)Google Scholar