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.

Digitalization of society brought improvement in many aspects of life but it also brought new cybersecurity challenges. The number of sophisticated, targeted cyber attacks is increasing, which requires constant improvements in Cybersecurity education. Despite this pressing need, the cybersecurity workforce gap is getting bigger. This paper presents a new approach for dynamic cybersecurity curriculum development that utilizes keyword extraction from various sources such as job ads, courses, and curricula with machine learning to quantify curriculum alignment with cybersecurity industry demands and address the workforce gap. The analysis illustrates curricula in the Central East Europe (CEE) region, maps cyber security job ads to curricula, and quantifies coverage of courses, industry, and reference framework topics based on keyword matching. The case study conducted with curricula from CEE illustrates coverage according to the ENISA's European Cybersecurity Skills Framework (ECSF) roles and optimization progress after adjustment application. The results demonstrate the importance of dynamic curriculum updates for academic institutions including cybersecurity workforce gap reduction and lack of real progress towards alignment with ECSF.

Various publicly available studies show that millions of cybersecurity experts are missing worldwide. One possible way to tackle the workforce gap is with tailored higher education programmes. The goal of this paper is to present the relevant projects and frameworks of the European Union which can guide the development of novel cybersecurity education offerings. We describe the most relevant and freely available tools and test them in the development of a joint Master study programme to be offered by a consortium of five European universities. We show that these tools allow educators and study programme developers to map their outputs to the European Cybersecurity Framework developed by the ENISA and other similar frameworks. We complete our work with a detailed analysis of a joint cybersecurity master programme consisting of four innovative and distinctly different tracks.

Demand for cybersecurity experts is high, driven by the increasing digitization of society and increasing number of sophisticated, targeted cyber attacks. Despite this pressing need, a significant shortfall in the number of cybersecurity experts remains due to very diverse landscape of knowledge and complex curriculum accreditation process. In this paper, we present a new model for curriculum analysis and adjustment that addresses entire curricula or course material. It employs machine learning and text-mining techniques for keyword extraction and further comparison with reference skills frameworks. The analysis illustrates a new measurement that quantifies coverage of cybersecurity role and its importance within curriculum based on keyword matching. The case study was conducted with university curricula from Europe and North America. The results illustrate curriculum coverage according to the ENISA's European Cybersecurity Skills Framework (ECSF) roles and optimization progress after our method application.

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.

Web archives store born-digital documents, which are usually collected from the Internet by crawlers and stored in the Web Archive (WARC) format. The trustworthiness and integrity of web archives is still an open challenge, especially in the news portal domain, which face additional challenges of censorship even in democratic societies. The aim of this paper is to present a light-weight, blockchain-based solution for web archive validation, which would ensure that documents retrieved by crawlers are authentic for many years to come. We developed our archive validation solution as an extension and continuation of our work in web crawler development mainly targeting news portals. The system is designed as an overlay over a blockchain with a proof-of-stake (PoS) distributed consensus algorithm. PoS was chosen due to its lower ecological footprint compared to proof-of-work solutions (e.g. Bitcoin) and lower expected investment in computing infrastructure. We based our prototype on the open-source Nxt blockchain and implemented it in Python. The prototype was tested on web archive content crawled from Hungarian news portals at two different timestamps with more than 1 million articles in total. We concluded that the proposed solution is accessible, usable by different stakeholders to validate crawled content, deployable on cheap commodity hardware, tackles the archive integrity challenge and is capable to efficiently manage duplicate documents.

This paper presents an algorithm for one-line diagram generation of multiple interconnected, i.e. weakly meshed, feeders supplied by one distribution substation. The substation area is modeled with a mathematical graph, which is then prepared and drawn in multiple steps. A vertex propagation algorithm is applied starting at the substation vertex to traverse the whole graph and disconnect interconnected feeders at normal open points (NOP), thereby creating a graph without loops. In the second step, the optimal feeder ordering is determined by the genetic algorithm (GA) based on the analysis of NOPs. In the third step each feeder is visualized recursively, with respect to the sub-graph direction based on NOPs. In the fourth and last step the NOPs are reconnected with orthogonal edges, and by inserting artificial vertices wherever necessary. The algorithm was tested on multiple, weakly meshed distribution substation areas extracted from several different European-style distribution network models. It generated visually pleasing one-line diagrams for systems with more than 1000 objects. The quality of the generated one-line diagrams was assessed by a formula, which took into account different characteristics, e.g. diagram area, total edge length, number of crossings.

The primary goal of this paper is to investigate what could have made successful Google's OpenSpot, a crowdsensing based parking assistance application cancelled in 2012. For this analysis we have developed UPark, a simulation environment capable of simulating large numbers of vehicles, which look for optimal parking spots in busy urban environments and whose drivers might inform others when they take or free a parking spot (e.g. via a mobile crowdsensing based application which allows the drivers to share the exact time and GPS position of a parking event). We introduce also the UPark simulation environment which has the potentials to be used for investigating other crowdsensing scenarios, e.g. `sensing' the perceived safety level or crowdedness of urban spaces, or public transport related events.

This paper analyzes various algorithms for the automatic generation of electric power system one-line diagrams. Historically these one-line diagrams were created manually. As these diagrams can be large, their manual creation takes a lot of time and resources, and it is also prone to errors. One possible solution to this problem is the automatic generation of one-line diagrams. Automatic visualization can be performed by existing rule-based algorithms, or if the network is modeled by a mathematical graph, then the problem can also be solved by graph drawing algorithms. Initial results achieved with soft computing algorithms will also be shown. We discuss the similarities, differences and applicability of these different algorithms in automatic one-line diagram generation. The final goal is to find the most suitable algorithm for the automatic generation of visually pleasing one-line diagrams, which allow dispatchers and engineers working in control centers a higher level of efficiency in performing their everyday tasks.

Grid computing is the future computing paradigm for enterprise applications. It can be used for executing large scale workflow applications. This paper focuses on the workflow scheduling mechanism. Although there is much work on static scheduling approaches for workflow applications in parallel environments, little work has been done on a Grid environment for industrial systems. Utility Management Systems (UMS) are executing very large numbers of workflows with very high resource requirements. Unlike the grid approach for standard scientific workflows, UMS workflows have a different set of requirements and thereby optimization of resource usage has to be made in a different way. This paper proposes a novel scheduling architecture which dynamically executes a scheduling algorithm using near real-time feedback from the execution monitor. An Artificial Neural Network was used for workflow scheduling and performance tests show that significant improvement of overall execution time can be achieved by this soft-computing method.