@inbook{527, keywords = {Network, Attack. Taxonomy, Ontology}, author = {Renier van Heerden and Louise Leenen and Barry Irwin}, title = {Description of a Network Attack Ontology Presented Formally}, abstract = {

The identification of network attacks in real-time is becoming increasingly important. Most Artificial Intelligence (AI) applications use machine learning to do the classification of attack types but the advantage of an ontological approach is that automated reasoning is the underpinning theory rather than automated learning. Automated reasoners allow automated classification and this powerful feature is the basis for the developing of an early warning system for active network attacks. In this paper, the authors describe how to employ Semantic Technologies by building an ontology to identify network attack types in order to support the automated classification of current network attacks by recognising relevant properties which are then mapped to relevant attack scenarios depicted in the ontology. The classes and relationships of the ontology are described formally and implemented in Prot{\'e}g{\'e}, an ontology editor. The Attack Scenario class, a core class of the ontology, represents types of network attacks, for example, a Denial of Service attack. The ontology is evaluated by showing three examples of real attacks that are correctly classified by the presented ontology.

}, year = {2021}, journal = {Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities}, edition = {1}, chapter = {343{\textendash}368}, pages = {X, 467}, month = {01 June 2021}, publisher = {Springer, Cham}, issn = {1860-9503}, isbn = {978-3-030-72236-4}, doi = {https://doi.org/10.1007/978-3-030-72236-4_14}, }