Knowledge discovery from big data for intrusion detection using LDA

Proceedings - 2014 IEEE International Congress on Big Data, BigData Congress 2014

Volumn , Issue , 2014, Pages 760-761

  Huang, Jingwei ^a   Kalbarczyk, Zbigniew ^a   Nicol, David M ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

big data; data mining; intrusion detection; LDA

Indexed keywords

BIG DATA; DATA MINING; LEARNING ALGORITHMS; STATISTICS;

HYBRID APPROACH; LARGE VOLUMES; LATENT DIRICHLET ALLOCATIONS; LDA MODELS; NEW APPROACHES; TOPIC MODELING;

INTRUSION DETECTION;

EID: 84923865799     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BigData.Congress.2014.111     Document Type: Conference Paper

References (5)

1. A. Sharma, Z. Kalbarczyk, J. Barlo w, and R. Iyer, " Analysis of security data from a large computing organization," in Proc. DSN'2011, pp. 506-517.
2. A. Pecchia, A. Sharma, Z. Kalbarczyk, D. Cotroneo, and R. K. Iyer, " Identifying compromised users in shared computing infrastructures: A data-driven bayesian network approach," in Proc. SRDS'2011, pp. 127-136.
3. P. Garcia-Teodoro, J. Diaz-Verdejo, G. Macia-Fernandez, and E. Vazquez, " Anomaly-based network intrusion detection: Techniques, systems and challenges," Computers & Security, vol. 28, pp. 18-28, 2009.
4. D. M. Blei, "Probabilistic topic models, " Commun. AC M, vol. 55, no. 4, pp. 77-84, 2012.
5. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation, " J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003.