2,510 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms
In this paper we present the design and evaluation of intrusion detection
models for MANETs using supervised classification algorithms. Specifically, we
evaluate the performance of the MultiLayer Perceptron (MLP), the Linear
classifier, the Gaussian Mixture Model (GMM), the Naive Bayes classifier and
the Support Vector Machine (SVM). The performance of the classification
algorithms is evaluated under different traffic conditions and mobility
patterns for the Black Hole, Forging, Packet Dropping, and Flooding attacks.
The results indicate that Support Vector Machines exhibit high accuracy for
almost all simulated attacks and that Packet Dropping is the hardest attack to
detect.Comment: 12 pages, 7 figures, presented at MedHocNet 200
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