Organizational Unit: SECURITATE CIBERNETICA SI INFRASTRUCTURI CRITICE
A review of machine learning techniques for the cybersecurity of critical infrastructures
(2020) Cîrnu, Carmen Elena; Florian, Vladimir; Vevera, Adrian Victor; Ciupercă, Ella Magdalena; Stanciu, Alexandru; SECURITATE CIBERNETICA SI INFRASTRUCTURI CRITICE
An essential component of the National security consists of the protection of its critical infrastructures (CIs), whether they are physical or virtual, as any disruption of their services could have a serious impact on economic well-being, public health or safety, or any combination of these. Any shutdown or delay may determine financial losses and major risks to people and the environment. All modern CIs are controlled by Industrial Control Systems (ICS) being dependent on their correct and continuous undisturbed functioning. Modern ICSs are inherently much less secure and exposed to the majority of cyber-attacks that are becoming more advanced and sophisticated. Consequently, efficient tools for the protection of hardware and software components of ICSs are required. One such class consists of intrusion prevention and detection systems (IPDS). Contemporary IPDSs use machine learning algorithms to detect threats manifested as anomalous behavior of a particular system. To provide robust detection systems with sufficient layers of protection, these must be combined with other methods and extensively tested with good datasets and using appropriate testbeds. Recent research suggests that conventional intrusion detection approaches are unable to cope with the complexity and ever-changing nature of industrial intrusion attacks. Moreover, deep learning methods are achieving state-of-the-art results across a range of difficult problem domains. The objective of our paper is to identify and discuss machine learning-based intrusion detection and protection methods and their implementation in industrial control intrusion detection systems, able to contribute to ensuring national security.
Variables of Stem Career of Women in Romania
(2020) Ciupercă, Ella Magdalena; Stanciu, Alexandru; Stanciu, Alexandru; SECURITATE CIBERNETICA SI INFRASTRUCTURI CRITICE
Participation in the multicultural conference of TECIS 2019 in Sozopol, Bulgaria, brought to the forefront of the participants' discussions a topic at the same time important and paradoxical for the 21st century: the marginalization of women in the fields of STEM (science, technology, engineering, mathematics). To discuss the topic, an ad hoc TECIS Inclusion and Diversity Working Group was set up, with 23 researchers from over 10 countries. The details provided by the participants regarding the situation in the country of origin have entrusted the researchers that this phenomenon is a versatile and extremely complex one. And yet, the difficulties encountered by women represented a common denominator; that is why the identification of the causes that led to the perpetuation of such traditionalist conditions in fields of accelerated modernization of society became a priority. In order to achieve the main objective of the Working Group, which is to work on building community and peer support, the first necessary and important step is to understand the current status of STEM in each country. To complete this stage, in this paper, we will perform a secondary analysis of the available statistical data for Romania to identify Romanian cultural specific in the field of participation of women in STEM education and STEM labor force. Further, we will develop a series of hypotheses that will underpin future studies.
Descriere serviciu Protectie Infrastructuri Critice.