Third International Conference on Natural Computation (ICNC 2007) 2007
DOI: 10.1109/icnc.2007.573
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Prior Knowledge SVM-based Intrusion Detection Framework

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Cited by 3 publications
(4 citation statements)
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“…Many anomaly NIDSs, e.g., [2], [3], [6], [7], [10], in the literature have applied KDD CUP99 datasets [17] in their experiments: All belong to offline detections because many of the 41 features proposed by KDD CUP99 are content-or connectionbased [2], [18]. This study focuses on online real-time response to anomaly traffic caused by DoS or Worm flooding attacks.…”
Section: Discussionmentioning
confidence: 99%
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“…Many anomaly NIDSs, e.g., [2], [3], [6], [7], [10], in the literature have applied KDD CUP99 datasets [17] in their experiments: All belong to offline detections because many of the 41 features proposed by KDD CUP99 are content-or connectionbased [2], [18]. This study focuses on online real-time response to anomaly traffic caused by DoS or Worm flooding attacks.…”
Section: Discussionmentioning
confidence: 99%
“…Many approaches have been proposed in previous literature concerning the design of anomaly NIDSs, such as neuro-fuzzy [2], support vector machine [6], decision tree [7], Bayesian neural networks [8], Naive Nayes [9], genetic-fuzzy [3], [10], and fuzzy association rules [11]- [16]. However, to the best of our knowledge, all anomaly NIDSs emphasize effectiveness, but neglect efficiency.…”
Section: Introductionmentioning
confidence: 98%
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