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Open AccessArticle

Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model

Technical University of Košice, Faculty of Electrical Engineering and Informatics, Department of Cybernetics and Artificial Intelligence, Letna 9, Košice, 040 01, Slovakia
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 203;
Received: 31 December 2019 / Revised: 15 January 2020 / Accepted: 25 January 2020 / Published: 1 February 2020
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches.
Keywords: intrusion detection; machine learning; classification; knowledge modelling intrusion detection; machine learning; classification; knowledge modelling
MDPI and ACS Style

Sarnovsky, M.; Paralic, J. Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model. Symmetry 2020, 12, 203.

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