Theoretical and Applied Informatics

The paper presents a new ontology-based approach to the elaboration and management of evidences prepared by developers for the IT security evaluation process according to the Common Criteria standard. The evidences concern the claimed EAL (Evaluation Assurance Level) for a developed IT product or system, called TOE (Target of Evaluation), and depend on the TOE features and its development environment. Evidences should be prepared for the broad range of IT products and systems requiring assurance. The selected issues concerning the author’s elaborated ontology are discussed, such as: ontology domain and scope definition, identification of terms within the domain, identification of the hierarchy of classes and their properties, creation of instances, and an ontology validation process. This work is aimed at the development of a prototype of a knowledge base representing patterns for evidences.

In the article we study a model of TCP connection with Active Queue Managementin an intermediate IP router. We use the fluid flow approximation technique to model the interactions between the set of TCP flows and AQM algoithms. Computations for fluid flow approximation model are performed in the CUDA environment.

The article presents a method for 3D point cloud segmentation. The point cloud comes from a FARO LS scanner – the device creates a dense point cloud, where 3D points are organized in the 2D table. The input data set consists of millions of 3D points – it makes widely known RANSAC algorithms unusable. We add some modifi cations to use RANSAC for such big data sets.

Traffic classification is an important tool for network management. It reveals the source of observed network traffic and has many potential applications e.g. in Quality of Service, network security and traffic visualization. In the last decade, traffic classification evolved quickly due to the raise of peer-to-peer traffic. Nowadays, researchers still find new methods in order to withstand the rapid changes of the Internet. In this paper, we review 13 publications on traffic classification and related topics that were published during 2009-2012. We show diversity in recent algorithms and we highlight possible directions for the future research on traffic classification: relevance of multi-level classification, importance of experimental validation, and the need for common traffic datasets.