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Volumn 29, Issue 1, 2010, Pages 35-44

Reducing false positives in intrusion detection systems

Author keywords

Alarms' distribution; False alarms; Filter; Intrusion detection systems; Snort

Indexed keywords

DATA SETS; EVALUATION RESULTS; FALSE ALARMS; FALSE POSITIVE; FILTER ARCHITECTURE; IN-NETWORK; INTRUSION DETECTION SYSTEMS; POST-PROCESSING FILTERS; STATISTICAL PROPERTIES; THREE COMPONENT;

EID: 71649091715     PISSN: 01674048     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cose.2009.07.008     Document Type: Article
Times cited : (82)

References (19)
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    • Brugger T, Chow J. An assessment of the DARPA IDS evaluation dataset using Snort. UC Davis Technical Report CSE-2007-1, Davis; 2007.
    • (2007) UC Davis Technical Report
    • Brugger, T.1    Chow, J.2
  • 7
    • 0345438685 scopus 로고    scopus 로고
    • Roc graphs: Notes and practical considerations for researchers
    • Technical report;
    • Fawcett T. Roc graphs: notes and practical considerations for researchers. Technical report; 2003.
    • (2003)
    • Fawcett, T.1
  • 9
    • 3142623031 scopus 로고    scopus 로고
    • Clustering intrusion detection alarms to support root cause analysis
    • Julisch K. Clustering intrusion detection alarms to support root cause analysis. ACM Trans Inf Syst Secur 6 4 (2003) 443-471
    • (2003) ACM Trans Inf Syst Secur , vol.6 , Issue.4 , pp. 443-471
    • Julisch, K.1
  • 15
    • 27644590551 scopus 로고    scopus 로고
    • Data mining and machine learning-towards reducing false positives in intrusion detection
    • Pietraszek T., and Tanner A. Data mining and machine learning-towards reducing false positives in intrusion detection. Inform Secur Tech Rep 10 3 (2005) 169-183
    • (2005) Inform Secur Tech Rep , vol.10 , Issue.3 , pp. 169-183
    • Pietraszek, T.1    Tanner, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.