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» Combined Data Mining Approach for Intrusion Detection
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CCS
2009
ACM
14 years 3 months ago
Active learning for network intrusion detection
Anomaly detection for network intrusion detection is usually considered an unsupervised task. Prominent techniques, such as one-class support vector machines, learn a hypersphere ...
Nico Görnitz, Marius Kloft, Konrad Rieck, Ulf...
INCDM
2010
Springer
125views Data Mining» more  INCDM 2010»
13 years 10 months ago
Web-Site Boundary Detection
Defining the boundaries of a web-site, for (say) archiving or information retrieval purposes, is an important but complicated task. In this paper a web-page clustering approach to...
Ayesh Alshukri, Frans Coenen, Michele Zito
ACSAC
2007
IEEE
14 years 3 months ago
Countering False Accusations and Collusion in the Detection of In-Band Wormholes
Cooperative intrusion detection techniques for MANETs utilize ordinary computing hosts as network intrusion sensors. If compromised, these hosts may inject bogus data into the int...
Daniel Sterne, Geoffrey Lawler, Richard Gopaul, Br...
TSMC
2002
134views more  TSMC 2002»
13 years 8 months ago
Incorporating soft computing techniques into a probabilistic intrusion detection system
There are a lot of industrial applications that can be solved competitively by hard computing, while still requiring the tolerance for imprecision and uncertainty that can be explo...
Sung-Bae Cho
KDD
2002
ACM
157views Data Mining» more  KDD 2002»
14 years 9 months ago
Learning nonstationary models of normal network traffic for detecting novel attacks
Traditional intrusion detection systems (IDS) detect attacks by comparing current behavior to signatures of known attacks. One main drawback is the inability of detecting new atta...
Matthew V. Mahoney, Philip K. Chan