Abstract
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 Naïve 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.
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Keywords
- Support Vector Machine
- Gaussian Mixture Model
- Classification Algorithm
- Intrusion Detection
- Intrusion Detection System
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© 2008 International Federation for Information Processing
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Mitrokotsa, A., Tsagkaris, M., Douligeris, C. (2008). Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms. In: Cuenca, P., Guerrero, C., Puigjaner, R., Serra, B. (eds) Advances in Ad Hoc Networking. Med-Hoc-Net 2008. IFIP International Federation for Information Processing, vol 265. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09490-8_12
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DOI: https://doi.org/10.1007/978-0-387-09490-8_12
Publisher Name: Springer, Boston, MA
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