Content Based Model Transformations: Solutions to Existing Issues with Application in Information Security

Model-Driven Engineering uses models in various stages of the software engineering. To reduce the cost of modelling and production, models are reused by transforming. Therefore the accuracy of model transformations plays a key role in ensuring the quality of the process. However, problems exist when trying to transform a very abstract and content dependent model. This paper describes the issues arising from such transformations. Solutions to solve problems in content based model transformation are proposed as well. The usage of proposed solutions allowing realization of semi-automatic transformations was integrated into a tool, designed for OPC/XML drawing file transformations to CySeMoL models. The accuracy of transformations in this tool has been analyzed and presented in this paper to acquire data on the proposed solutions influence to the accuracy in content based model transformation

Security Ontology for Adaptive Mapping of Security Standards

Adoption of security standards has the capability of improving the security level in an organization as well as to provide additional benefits and possibilities to the organization. However mapping of used standards has to be done when more than one security standard is employed in order to prevent redundant activities, not optimal resource management and unnecessary outlays. Employment of security ontology to map different standards can reduce the mapping complexity however the choice of security ontology is of high importance and there are no analyses on security ontology suitability for adaptive standards mapping. In this paper we analyze existing security ontologies by comparing their general properties, OntoMetric factors and ability to cover different security standards. As none of the analysed security ontologies were able to cover more than 1/3 of security standards, we proposed a new security ontology, which increased coverage of security standards compared to the existing ontologies and has a better branching and depth properties for ontology visualization purposes. During this research we mapped 4 security standards (ISO 27001, PCI DSS, ISSA 5173 and NISTIR 7621) to the new security ontology, therefore this ontology and mapping data can be used for adaptive mapping of any set of these security standards to optimize usage of multiple securitystandards in an organization

Travel Direction Recommendation Model Based on Photos of User Social Network Profile

Travelling is one of the most enjoyable activities for people of all ages. It is constantly looking for innovative solutions on how to tailor travel recommendations to the needs of its customers. The purpose of our proposed recommendation model is to suggest travelling countries based on photos from the user’s social network account and metadata associated with the photos. Such recommendation models are highly dependent on the data used in the model preparation steps and on the technologies and methods implemented in the model. The newly collected data from the Instagram users’ accounts were used in the model preparation. The recommendation system is based on the combination of four methods: object detection, similarity measures, classification, and data clustering. The novelty of the proposed recommendation model is that it adopts different data (Instagram photos) for travel direction recommendation, defines a new combined method, integrates results of similarity measurement and SOM application results into one final recommendation, and estimates the parameter impact for different components of recommendation model. A proposed evaluation measure has been used to conclude the results of the recommendation model and as a result the names of the travelling countries have been recommended. The results of the proposed recommendation model are promising, and the validation results demonstrate that on average 63% of the users who visited countries match the recommendations provided for the trip directions, while the accuracy of recommendations, matching user visited countries, but not presented in the photos for recommendation estimation, on average was 96%. The accuracy performance is very positive, while the recommendation system is fully automated and machine learning based. With time, the accuracy of the model may even increase by adopting the photo metadata (location)