SCOPUS 정보 검색 플랫폼

IEEE International Conference on Intelligence and Security Informatics, 2008, IEEE ISI 2008

Volumn , Issue , 2008, Pages 251-253

Anomaly detection in high-dimensional network data streams: A case study

(3) Zhang, Ji a Gao, Qigang a Wang, Hai b

a DALHOUSIE UNIVERSITY (Canada)

b SAINT MARY S UNIVERSITY (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; FINANCIAL DATA PROCESSING; INTERSYMBOL INTERFERENCE; RIVERS;

ANOMALY DETECTION METHODS; ANOMALY DETECTIONS; CASE STUDIES; FALSE-POSITIVE REDUCTION; HIGH-DIMENSIONAL; HIGH-DIMENSIONAL DATA; INNOVATIVE APPROACHES; INTERNATIONAL CONFERENCES; NETWORK DATA; SECURITY INFORMATICS; TRAINING DATA;

INTRUSION DETECTION;

EID: 51849128252 PISSN: None EISSN: None Source Type: Conference Proceeding
DOI: 10.1109/ISI.2008.4565071 Document Type: Conference Paper

Times cited : (8)

References (9)

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2
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- Breunig, M.M.¹ Kriegel, H.-P.² Ng, R.T.³ Sander, J.⁴

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- Cui, H.¹

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- A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data
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- Eskin, E.¹ Arnold, A.² Prerau, M.³ Portnoy, L.⁴ Stolfo, S.⁵

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- Finding intensional knowledge of distance-based outliers
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- Knorr, E.M.¹ Ng, R.T.²

6
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- A novel method for detecting outlying subspaces in high-dimensional databases using genetic algorithm
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- Detecting outlying subspaces for high-dimensional data: The new task, algorithms, and performance
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.