A hybrid machine learning approach to network anomaly detection-Reference-Cited by-同舟云学术

A hybrid machine learning approach to network anomaly detection

Published:2007-09 Issue:18 Volume:177 Page:3799-3821
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Shon Taeshik,Moon Jongsub

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference47 articles.

1. K. Ahsan, D. Kundur, Practical Data Hiding in TCP/IP, in: Proc. Workshop on Multimedia Security at ACM Multimedia, French Riviera, 2002, pp. 7–19.

2. Detecting Unusual Program Behavior Using the Statistical Component of the Next-Generation Intrusion Detection, SRI-CSL-95-06;Anderson,1995

3. O. Arkin., xprobe, 2004, .

4. Locally weighted learning for control;Atkeson;AI Review,1996

5. Supervised learning on a fuzzy Petri net;Konar;Information Sciences,2005

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