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(PDF) An Imunogenetic Technique To Detect Anomalies In ...
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The paper describes an immunogenetic approach which can detect a wide variety of intrusive ac- tivities on networked computers.
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An immunogenetic technique to detect anomalies in network traffic ...
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The paper describes an immunogenetic approach which can detect a wide variety of intrusive activities on networked computers. In particular, this technique ...
An Imunogenetic Technique To Detect Anomalies In Network ...
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An immunogenetic approach which can detect a wide variety of intrusive activities on networked computers inspired by the negative selection mechanism of the ...
An immunogenetic technique to detect anomalies in ...
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由 FA González 著作2002被引用 98 次 — The novel pat- tern detectors (in the complement space) are evolved using a genetic search, which could differentiate varying degrees of abnormality in network ...
An Imunogenetic Technique To Detect Anomalies In Network ...
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The paper describes an immunogenetic approach which can detect a wide variety of intrusive activities on networked computers. In particular, this technique ...
An imunogenetic technique to detect anomalies in network ...
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Abstract The paper describes an immunogenetic approach which can detect a wide variety of intrusive activities on networked computers.
An Imunogenetic Technique To Detect Anomalies In Network ...
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The paper describes an immunogenetic approach which can detect a wide variety of intrusive activities on networked computers. In particular, this technique ...
an approach to detect anomalies in network traffic using ...
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2024年10月16日 — This article delves into the application of machine learning techniques for anomaly detection in network traffic. We discuss different ...
Anomaly Detection Using Real-Valued Negative Selection
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由 FA González 著作2003被引用 502 次 — This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection.
Detecting Anomalies in Network Traffic Using Maximum ...
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由 Y Gu 著作被引用 544 次 — We develop a behavior-based anomaly detection method that detects network anomalies by comparing the current network traffic against a baseline distribution.
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