The large majority of the current cybersecurity technologies are identifying cyberattacks based on predefined signatures, rules, filters or scenarios such as virus or malware definitions, firewall rules, intrusion prevention filters or SIEM playbooks. In recent years, these technologies have achieved only a small reach towards addressing the new challenges and the pace of their development is bowing in front of the much faster evolution of the threats. This exponential increase in the number and complexity of cyberattacks opened the door to the utilization of artificial intelligence algorithms within the protection technologies primarily for a couple of reasons: to identify deviations from trained regular behavior and to analyze large volumes of data for patterns. Following this cycle, in turn, cyberattacks started themselves to employ artificial intelligence to automate labor-intensive tasks like social media and other public information analysis to prioritize targets, to evade current techniques, to generate domains or to perform human-like operations. These new trends started to challenge the current static-rule-based approach largely used today in the cybersecurity ecosystem and at the same time to provide the opportunity to adapt the current technologies and sharing platforms to respond to these dynamic threats. The learning processes needs modifications as well by employing mechanisms that will allow to deliver knowledge faster and more specialized. These developments in the cybersecurity realm led not only to the adaptation of the existing technologies but also to the creation of new trends, new technologies and new products that could offer alternative protection against this fast changing threat landscape.

Using the Activity Theory to Identify the Challenges of Designing eLearning Tools based on Machine Learning for Security Operations Centers Mihail CAZACU Economic Informatics Doctoral School, University of Economic Studies, Romania, Romana Square 6, Bucharest, Romania mihail.cazacu@gmail.com Maria-Iuliana DASC?LU Department of Engineering in Foreign Languages, Faculty of Engineering in Foreign Languages, University Politehnica of Bucharest, Splaiul Independentei, No 313, Bucharest, Romania maria.dascalu@upb.ro Constanta-Nicoleta BODEA Department of Economic Informatics and Cybernetics, Faculty of Economic Cybernetics, Statistics and Informatics, University of Economic Studies, Romana Square 6, Bucharest, Romania bodea@ase.ro Abstract There is a fast-growing requirement for setting up Security Operation Centers (SOCs), with qualified personnel, mainly due to the increase of demands to protect ITC systems from security breaches, data disruption or unauthorized usage. The 2018 Report of Privacy Rights Clearinghouse mentions that over 8,000 data breaches were reported since 2005, with more than 10 billion records affected. And according to the 2017 study of IBM Security and Ponemon Institute, the average cost of a data breach exceed 3.6 million US dollars. SOCs have the mission to run in this "arms race" against cyber attackers (criminals, spies, terrorists, activists) and to be economically viable, as a profit or a cost center. Development of e-learning tools for continuous enhancing of the professional competences of the SOC's personnel is critical for the successful operation of SOCs. Recent studies have applied the framework of the Activity Theory in order to identify the conflicting priorities which need to be handled by different members of SOCs and have suggested ways to mitigate the risks. While automating mundane tasks is one solution, the issue of automating the automation process itself through Machine Learning, especially in the e-learning activities performed inside SOCs was not often addressed. The paper aims to present the challenges of applying the framework of the Activity Theory in designing e-learning tools based on machine learning methods for SOCs. Some well-established Open Source security tools and machine learning packages will be evaluated for their suitability for developing e-learning tools.

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Business process management (BPM) tools allow the analysis and improvement of the actual business processes in the organization, for making them more efficient and effective. The paper presents how the discipline of BPM can be applied to the project processes, by adopting the automation in different stage of the project process management, especially in the process analysis and discovery. The automated project process discovery is possible due to the extended IT infrastructure for project process implementation, available in the digital era. While the process simulation for project process analysis is already applied on large scale, the project process mining is still not so much adopted and exploited.The authors investigate how the existing BPM tools can be used for project process modeling and the automated discovery. In the last part of the paper, the authors present some proposals for future development of these applications.

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