Online One-class Classification for Intrusion Detection Based on the Mahalanobis Distance
Résumé
Machine learning techniques have been very popular in the past decade for their ability to detect hidden patterns in large volumes of data. Researchers have been developing online intrusion detection algorithms based on these techniques. In this paper, we propose an online one-class classification approach based on the Mahalanobis distance which takes into account the covariance in each feature direction and the different scaling of the coordinate axes. We define the one-class problem by two concentric hyperspheres enclosing the support vectors of the description. We update the classifier at each time step. The tests are conducted on real data.
Mots clés
one-class
cybersecurity
computer crime
critical infrastructures
firewalls
learning (artificial intelligence)
pattern classification
principal component analysis
radial basis function networks
SCADA systems
support vector machines
lp-norms
information and communication technologies
supervisory control and data acquisition systems
cyberattacks heterogeneity
SCADA networks
systems vulnerabilities
intrusion detection systems
IDS
cyberattacks modeling
malicious intrusions detection
one-class classification algorithms
support vector data description
SVDD
kernel principle component analysis
radial basis function kernels
RBF kernels
bandwidth parameter
Kernel
Machine learning
Intrusion detection
Optimization
kernel methods
\mbi lₚ -norms
one-class classification
supervisory control and data acquisition (SCADA) systems
Mahalanobis distance
| Origine | Fichiers produits par l'(les) auteur(s) |
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