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 security standards in an organization.

Spamming and phishing are two types of emailing that are annoying and unwanted, differing by the potential threat and impact to the user. Automated classification of these categories can increase the users’ awareness as well as to be used for incident investigation prioritization or automated fact gathering. However, currently there are no scientific papers focusing on email classification concerning these two categories of spam and phishing emails. Therefore this paper presents a solution, based on email message body text automated classification into spam and phishing emails. We apply the proposed solution for email classification, written in three languages: English, Russian, and Lithuanian. As most public email datasets almost exclusively collect English emails, we investigate the suitability of automated dataset translation to adapt it to email classification, written in other languages. Experiments on public dataset usage limitations for a specific organization are executed in this paper to evaluate the need of dataset updates for more accurate classification results.

Preparation for potential threats is one of the most important phases ensuring system security. It allows evaluating possible losses, changes in the attack process, the effectiveness of used countermeasures, optimal system settings, etc. In cyberattack cases, executing real experiments can be difficult for many reasons. However, mathematical or programming models can be used instead of conducting experiments in a real environment. This work proposes a composite denial of service attack model that combines bandwidth exhaustion, filtering and memory depletion models for a more real representation of similar cyber-attacks. On the basis of the introduced model, different experiments were done. They showed the main dependencies of the influence of attacker and victim's properties on the success probability of denial of service attack. In the future, this model can be used for the denial of service attack or countermeasure optimization.

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

Information security is one of the key concerns of an enterprise or organization. To assure suitable management of information security a list of information security management frameworks has been developed by a number of institutions and authors. A condensed information in information security management framework is very important to a small and medium enterprise as this type of enterprise usually lacks resources for information security expertise and deep analysis. Despite the fact, the information security management process and its frameworks, on the other hand, are very complex and require a big number of different elements. At the moment the comparison it is very shallow, as all properties of the comparison are treated equally important. In real life, the importance of different criteria of information security management framework and their suitability for small and medium enterprise vary. Therefore we use the Analytic Hierarchy Process to construct a hierarchy of information security management frameworks quality and applicability in small and medium enterprise and define the weights for each of the criteria. Weighted criteria express the importance of the criteria and executed the final comparison of alternatives (five information security management frameworks) is more realistic (similar to experts opinion) comparing to existing comparisons.