Generate knowledge related to the application of Big Data solutions and artificial intelligence in the field of cybersecurity.


The Group in Data Analytics and Artificial Intelligence applied to Cybersecurity arises due to the need faced by Higher Education Institutions (HEIs), to counteract the various security attacks that put their information at risk, the vulnerability of their computer systems, the continuity of the service, and the effect on the institutional image. Also, the new processes of accreditation where the information security management processes are evaluated should be considered. In this sense, HEIs seek to establish strategies that allow them to strengthen the security of their information and focus on activities such as detecting intruders, detecting anomalies or detecting vulnerabilities. Under this approach, the main aspects to consider are the display of large-scale information, the various forms of information, and the handling of large volumes of information.

Solutions such as Big Data or the use of machine learning are alternatives to try to solve the problems of handling large amounts of information in real time. However, the implementation of this type of infrastructure requires specialized staff, time for implementation, and high investment, which is not feasible for all HEIs. In Ecuador the application of solutions such as Big Data, learning machines, and data analytics has taken on great importance in recent years, but there are still several fields of research on its applicability in scenarios where analytics are required to solve complex problems in almost real time. In terms of cybersecurity there are also advances such as the creation of policies or the implementation of security controls, however, data analysis is neglected in order to move from reactive processes to predictive processes that allow decisions to be made to reduce the impact of possible attacks.

Under this premise, the work group proposes the analysis of data analytics methodologies and architectures applied in conjunction with decision support systems, which will allow HEIs to take actions based on institutional knowledge. The main objective is to strengthen the generation of knowledge in the use of data mining algorithms and learning machines by analyzing scenarios where they can be more applicable. In addition, the work group proposes a Big Data architecture and learning machine that can be used by the different HEIs on demand without having to invest money. For this, security control in organizations using these new technological solutions, establishing methods of communication between the interested parties, collaborative processes between the security groups of the organizations, procedures for the collection, aggregation and analysis of the data, and the management of strategic indicators in cybersecurity need to be considered, in light of the principles of personal privacy and information transparency.


  • CEDIA Members

The design and implementation of an architecture and infrastructure of Big Data and Artificial Intelligence applied to cybersecurity will allow it to be used by the different member institutions, taking into account the importance of information security management for the protection of institutional informatics systems, organizational information, and personal information privacy. In the proposal of the new accreditation model for Higher Education Institutions, having information security policies and processes is another of the evaluation milestones.

  • Community

Public institutions that provide services to the community also need to have these new solutions that allow them to have a comprehensive vision of security in their organizations. Often, due to lack of budget or knowledge, they do not have these types of implementations. The development of technology in public services under the concepts of electronic government, open data, or smart cities, seeks to, proactively and predictively, strengthen the decision making process in organizations, against threats or security risks, while minimizing the impact on organizations and the citizen's privacy.


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