Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks

Proceedings of the ACM Symposium on Applied Computing

Volumn , Issue , 2008, Pages 976-980

  Spinosa, Eduardo J ^a   De Carvalho, André Ponce De Leon F ^a   Gama, João ^b  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSITY OF PORTO   (Portugal)

Author keywords

Clustering; Data streams; Novelty detection

Indexed keywords

COMPUTER HARDWARE DESCRIPTION LANGUAGES; COMPUTER NETWORKS; DATA MINING; LEARNING SYSTEMS;

CLASS STRUCTURES; CLUSTERING; DATA SETS; DATA STREAMS; INCREMENTAL LEARNINGS; NOVEL CONCEPTS; NOVELTY DETECTION;

INTRUSION DETECTION;

EID: 56749157104     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1363686.1363912     Document Type: Conference Paper

References (3)

1. C. Elkan. Results of the KDD'99 classifier learning. ACM SIGKDD Explorations, 1(2):63-64, 2000.
2. M. Markou and S. Singh. Novelty detection: a review -part 1: statistical approaches. Signal Processing, 83:2481-2497, 2003.
3. E. J. Spinosa, A. P. L. F. de Carvalho, and J. Gama. OLINDDA: A cluster-based approach for detecting novelty and concept drift in data streams. In 22nd Annual ACM Symposium on Applied Computing (SAC 2007), pages 448-452. ACM, 2007.