Cybersecurity has become important, especially during the last decade. The significant growth of information technologies, internet of things, and digitalization in general, increased the interest in cybersecurity professionals significantly. While the demand for cybersecurity professionals is high, there is a significant shortage of these professionals due to the very diverse landscape of knowledge and the complex curriculum accreditation process. In this article, we introduce a novel AI-driven mapping and optimization solution enabling cybersecurity curriculum development. Our solution leverages machine learning and integer linear programming optimization, offering an automated, intuitive, and user-friendly approach. It is designed to align with the European Cybersecurity Skills Framework (ECSF) released by the European Union Agency for Cybersecurity (ENISA) in 2022. Notably, our innovative mapping methodology enables the seamless adaptation of ECSF to existing curricula and addresses evolving industry needs and trend. We conduct a case study using the university curriculum from Brno University of Technology in the Czech Republic to showcase the efficacy of our approach. The results demonstrate the extent of curriculum coverage according to ECSF profiles and the optimization progress achieved through our methodology.

In late 2022, the novel European Cybersecurity Skills Framework (ECSF) was officially released by the European Union Agency for Cybersecurity (ENISA). It aims to connect cybersecurity education and training with practical needs of the job market. In particular, it maps role profiles, that reflect jobs, to the knowledge and skills they require. One of the first tools that demonstrated ECSF is the Curricula Designer web application that guides cybersecurity study program administrators in designing and analyzing their curricula. In this paper, we present a major update of the Curricula Designer tool. We develop a novel method for course scoring and quantitative analysis of curricula based on the European Credit Transfer and Accumulation System (ECTS) credits. We describe the underlying methods and show their practical implementation into the publicly-available web application. Furthermore, we update the definitions of the Skills, Knowledge and Role Profiles according to the latest ECSF definition and present the mappings in comprehensive matrices in the appendices.

We present the updated version of the Curricula Designer, a tool that is devoted to helping study program administrators and education providers to create cybersecurity curricula that are modern and reflect the needs of the job market. Our main contribution is the inclusion of the European Cybersecurity Skills Framework (ECSF) developed by ENISA to the Curricula Designer. The ECSF makes it possible to directly link knowledge and skills with professional profiles, which in turn reflect actual work roles on the job market. By adding ECSF to the Curricula Designer, we get a simple yet powerful tool that helps to identify the right content of cybersecurity curricula using rigorous, deterministic methods, applicable at any higher education provider. At the time of the paper submission, the Curricula Designer is the first practical application that is based on ECSF. However, due to its focus on practicality, usability and simplicity, we expect ECSF to become the dominant framework for cybersecurity knowledge and skills identification in Europe.

This article presents a new free web-based application, the Cybersecurity Job Ads Analyzer, which has been created to collect and analyse job adverts using a machine learning algorithm. This algorithm enables the detection of the skills required in advertised cybersecurity work positions. The application is both interactive and dynamic allowing for automated analyses and for the underlying database of job adverts to be easily updated. Through the Cybersecurity Job Ads Analyzer, it is possible to explore the skills required over time, and thereby enable academia and other training providers to better understand and address the needs of the industry. We will describe in detail the user interface and technical background of the application, as well as highlight the preliminary statistical results we have obtained from analysing the current database of job adverts.

The fashion industry is characterised by the need to make demand forecasts in advance and for highly volatile products for which we often have no sales history at the time the forecasts are made.For this reason, it is necessary to propose forecast mechanisms that can cope with the given conditions.Such forecasts can be based on expert predictions for generalized product categories.In this case, the task of machine learning forecasting methods would be to divide the aggregate prediction into forecasts for individual products, in each colour and size.In the paper, we present several approaches to this specific task.We present the use of the naive method, custom nearest neighbour approach, parametric linear mixed model and an ensemble approach.Overall, the best results we obtained for the ensemble method.Our research was based on real data from fashion retail.

The need to understand the decision bases of artificial intelligence methods is becoming widespread.One method to obtain explanations of machine learning models and their decisions is the approximation of a complex model treated as a black box by an interpretable rule-based model.Such an approach allows detailed and understandable explanations to be generated from the elementary conditions contained in the rule premises.However, there is a lack of research on the evaluation of such an approximation and the influence of the parameters of the rule-based approximator.In this work, a rulebased approximation of complex classifier for tabular data is evaluated.Moreover, it was investigated how selected measures of rule quality affect the approximation.The obtained results show what quality of approximation can be expected and indicate which measure of rule quality is worth using in such application.

SUMMARY: Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE-Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. AVAILABILITY AND IMPLEMENTATION: MAINE is freely available at maine.ibemag.pl as an online web application. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The paper aims at minimizing the skills gaps and skills shortages on the cybersecurity job market by empowering education and training institutions during the process of creation of new cybersecurity study programs. We provide a complex cybersecurity skills framework based on standardized definitions that helps with the identification of skills and knowledge necessary for cybersecurity work positions. Furthermore, we practically implement the framework in the form of an interactive web application for cybersecurity curricula design. The app, called Curricula Designer, is built upon the framework and allows intuitive design of higher-education curricula and their analysis with respect to requirements of work roles already defined in widely-accepted standards. Using the analytical functions, it is easy to identify missing content in the courses and precisely structure the study program so that the graduates are well-prepared to enter the job market. The Curricula Designer is described in details in this paper, including user interface and technical background, and a link for public free access is provided to serve all education and training institutions.

This paper investigates current application-independent slow Denial of Service (DoS) attacks. We propose Slowcomm and Slow Next attack models and present an attack simulation tool. We used this tool for vulnerability testing of several Internet services, including Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), and Secure Shell (SSH) servers. We also propose attack signatures and detection methods. We implemented these methods as an Intrusion Detection System (IDS) and tested them in an experimental network. Our testing revealed vulnerabilities in five of the six tested servers that caused the denial of service to legitimate users. Deployment of the proposed attack detector has shown a high detection success. We conclude that there is a need to increase the level of cybersecurity. Internet services are vulnerable to these new DoS attacks. Our analysis can be used for the security development of tested services. Our detector in combination with a network traffic filtering tool can be used to mitigate the attacks and keep the service available to Internet users.

Assuring the network traffic safety is a very important issue in a variety of today’s industries. Therefore, the development of anomalies and attacks detection methods has been the goal of analyses. In the paper the binary classification-based approach to network traffic safety monitoring is presented. The well known methods were applied to artificially modified network traffic data and their detection capabilities were tested. More detailed interpretation of the nature of detected anomalies is carried out with the help of the XAI approach. For the purpose of experiments a new benchmark network traffic data set was prepared, which is now commonly available.

In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company.

Local access data network (LADN) is a promising paradigm to reduce latency, enable lowering energy consumption, and improve quality of service (QoS) for the Fifth Generation (5G) radio access network (RAN) supporting vehicle to everything (V2X) communications. To achieve optimum resource allocation and save energy by minimizing the activation of LADN servers in Cloud-RAN, some remote radio heads (RRHs) can be turned on or off depending on the traffic demand. In this paper, we investigate the problem of how to realize effective resource management in 5G RAN supporting V2X communications. More precisely, we first propose a formulation of the resource management problem as an optimization problem with the objective of minimizing the number of RRHs to be turned on subject to the uplink bandwidth constraints. We then use a fully-fledged professional software to solve our optimization problem and propose a solution with heuristic algorithms to deal with the complexity of the problem for large scenarios. Moreover, we analyze the impact of the density of vehicles on the computation time and the influence of the uplink data rate and vehicle densities on the number of active RRHs. Our numerical results show that our proposed model can efficiently utilize the resources and provide optimum vehicles-to-RRHs associations which lead to energy-savings. For instance, to serve 100 vehicles with aggregated uplink data rate equal to 100 [Mbps], the optimal associations save about 70% of the energy comparing to the strongest-signal associations. Furthermore, we obtain optimal results for the small size problem in reasonable computation times, which are around 50 [ms].

Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induction facilitates verifying hypotheses concerning data dependencies which are expected or of interest. The powerful and flexible experimental environment allows straightforward investigation of different induction schemes. The analysis can be performed in batch mode, through RapidMiner plug-in, or R package. The software is available at GitHub (https://github.com/adaa-polsl/RuleKit) under GNU AGPL-3.0 license.

BACKGROUND: Survival analysis is an important element of reasoning from data. Applied in a number of fields, it has become particularly useful in medicine to estimate the survival rate of patients on the basis of their condition, examination results, and undergoing treatment. The recent developments in the next generation sequencing open new opportunities in survival study as they allow vast amount of genome-, transcriptome-, and proteome-related features to be investigated. These include single nucleotide and structural variants, expressions of genes and microRNAs, DNA methylation, and many others. RESULTS: We present LR-Rules, a new algorithm for rule induction from survival data. It works according to the separate-and-conquer heuristics with a use of log-rank test for establishing rule body. Extensive experiments show LR-Rules to generate models of superior accuracy and comprehensibility. The detailed analysis of rules rendered by the presented algorithm on four medical datasets concerning leukemia as well as breast, lung, and thyroid cancers, reveals the ability to discover true relations between attributes and patients' survival rate. Two of the case studies incorporate features obtained with a use of high throughput technologies showing the usability of the algorithm in the analysis of bioinformatics data. CONCLUSIONS: LR-Rules is a viable alternative to existing approaches to survival analysis, particularly when the interpretability of a resulting model is crucial. Presented algorithm may be especially useful when applied on the genomic and proteomic data as it may contribute to the better understanding of the background of diseases and support their treatments.

This paper summarizes AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines which was held between October 5, 2015 and March 4, 2016 at the Knowledge Pit platform.It describes the scope and background of this competition and explains our research objectives which motivated the specific design of the competition rules.The paper also briefly overviews the results of this challenge, showing the way in which those results can help in solving practical problems related to the safety of miners working underground.In particular, our analysis focuses on applications of prediction models in order to facilitate the assessment of seismic hazards, in a situation when the exploration of a given working site has just started and there is very little historical data available.

Underground coal mining is a branch of an industry which safety of operation is very dependent on the natural hazards.A proper seismic event prediction is a significant aspect of building classification models from the real data, which can affect the coal mining safety increase.In this paper four models, built in a well known data mining environments, are presented.The obtained models, depending on a given implementation of popular methods, occurred comparable to the best results from the competition.

The paper presents the results of research related to the efficiency of the so-called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction. The stages of rule growing and pruning were considered along with the issue of conflict resolution which may occur during the classification. The work is the continuation of research on the efficiency of quality measures employed in sequential covering rule induction algorithm. In this paper we analyse only these quality measures (8 measures) which had been recognized as effective based on previous conducted research. The study was conducted on approximately 70 benchmark datasets related to classification, regression and survival analysis problems. In the comparisons we analyzed prognostic abilities of the induced rules as well as the complexity of the resulting rule-based data models.

This paper presents the DISESOR integrated decision support system.The system integrates data from different monitoring and dispatching systems and contains such modules as data preparation and cleaning, analytical, prediction and expert system.Architecture of the system is presented in the paper and a special focus is put on the presentation of two issues: data integration and cleaning, and creation of prediction model.The work contains also a case study presenting an example of the system application.

The paper presents the results of research related to the efficiency of the so called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction.The stages of rule growing and pruning were considered along with the issue of conflicts resolution which may occur during the classification.The work is the continuation of research on the efficiency of quality measures employed in sequential covering rule induction algorithm.In this paper we analyse only these quality measures (9 measures) which had been recognised as effective based on previously conducted research.

BACKGROUND: Machine learning techniques are known to be a powerful way of distinguishing microRNA hairpins from pseudo hairpins and have been applied in a number of recognised miRNA search tools. However, many current methods based on machine learning suffer from some drawbacks, including not addressing the class imbalance problem properly. It may lead to overlearning the majority class and/or incorrect assessment of classification performance. Moreover, those tools are effective for a narrow range of species, usually the model ones. This study aims at improving performance of miRNA classification procedure, extending its usability and reducing computational time. RESULTS: We present HuntMi, a stand-alone machine learning miRNA classification tool. We developed a novel method of dealing with the class imbalance problem called ROC-select, which is based on thresholding score function produced by traditional classifiers. We also introduced new features to the data representation. Several classification algorithms in combination with ROC-select were tested and random forest was selected for the best balance between sensitivity and specificity. Reliable assessment of classification performance is guaranteed by using large, strongly imbalanced, and taxon-specific datasets in 10-fold cross-validation procedure. As a result, HuntMi achieves a considerably better performance than any other miRNA classification tool and can be applied in miRNA search experiments in a wide range of species. CONCLUSIONS: Our results indicate that HuntMi represents an effective and flexible tool for identification of new microRNAs in animals, plants and viruses. ROC-select strategy proves to be superior to other methods of dealing with class imbalance problem and can possibly be used in other machine learning classification tasks. The HuntMi software as well as datasets used in the research are freely available at http://lemur.amu.edu.pl/share/HuntMi/.

The paper presents an algorithm of decision rules redefinition that is based on evaluation of the importance of elementary conditions occurring in induced rules. Standard and simplified (heuristic) indices of elementary condition importance evaluation are described. There is a comparison of the results obtained by both indices concerning classifiers quality and elementary condition rankings estimated by the indices. The efficiency of the proposed algorithm has been verified on 21 benchmark data sets. Moreover, an analysis of practical applications of the proposed methods for biomedical and medical data analysis is presented. The obtained results show that the redefinition reduces considerably a rule set needed to describe each decision class. Additionally, after the rule set redefinition negated elementary conditions may also occur in new rules.

In the paper we present CHIRA, an algorithm performing decision rules aggregation. New elementary conditions, which are linear combinations of attributes may appear in rule premises during the aggregation, leading to so-called oblique rules. The algorithm merges rules iteratively, in pairs, according to a certain order specified in advance. It applies the procedure of determining convex hulls for regions in a feature space which are covered by aggregated rules. CHIRA can be treated as the generalization of rule shortening and joining algorithms which, unlike them, allows a rule representation language to be changed. Application of presented algorithm allows one to decrease a number of rules, especially in the case of data in which decision classes are separated by hyperplanes not perpendicular to the attribute axes. Efficiency of CHIRA has been verified on rules obtained by two known rule induction algorithms, RIPPER and q-ModLEM, run on 18 benchmark data sets. Additionally, the algorithm has been applied on synthetic data as well as on a real-life set concerning classification of natural hazards in hard-coal mines.

This work describes a system which tries to join the advantages of rough sets methods and fuzzy sets methods to improve classification processes. The fuzzy set theory supports approximate reasoning and the rough sets theory is responsible for data analysis and processing of automatic fuzzy rules generation. This system was designed as a typical knowledge based system, which contains four main parts: rule extractor, knowledge base, inference engine and user interface.