Iryna Yevseyeva

dblp:06/2213 · DBLP profile ↗
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18ranked-venue papers
4as first author
6since 2021 · last 2023
0000-0002-1627-7624ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 2 first-author · 1 since 2021Security and privacy · 6 · 4 since 2021Databases, data management, data science and information retrieval · 3 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1Theory of computation · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1
YearPublicationVenuePosition
2023 Integrated cybersecurity methodology and supporting tools for healthcare operational information systems
Bruno Coutinho, João Ferreira 0001, Iryna Yevseyeva, Vitor Basto-Fernandes
Comput. Secur.3
2023 Scenario-based incident response training: lessons learnt from conducting an experiential learning virtual incident response tabletop exercise
abstract
Purpose This paper aims to discuss the experiences designing and conducting an experiential learning virtual incident response tabletop exercise (VIRTTX) to review a business's security posture as it adapts to remote working because of the Coronavirus 2019 (COVID-19). The pandemic forced businesses to move operations from offices to remote working. Given that this happened quickly for many, some firms had little time to factor in appropriate cyber-hygiene and incident prevention measures, thereby exposing themselves to vulnerabilities such as phishing and other scams. Design/methodology/approach The exercise was designed and facilitated through Microsoft Teams. The approach used included a literature review and an experiential learning method that used scenario-based, active pedagogical strategies such as case studies, simulations, role-playing and discussion-focused techniques to develop and evaluate processes and procedures used in preventing, detecting, mitigating, responding and recovering from cyber incidents. Findings The exercise highlighted the value of using scenario-based exercises in cyber security training. It elaborated that scenario-based incident response (IR) exercises are beneficial because well-crafted and well-executed exercises raise cyber security awareness among managers and IT professionals. Such activities with integrated operational and decision-making components enable businesses to evaluate IR and disaster recovery (DR) procedures, including communication flows, to improve decision-making at strategic levels and enhance the technical skills of cyber security personnel. Practical implications It maintained that the primary implication for practice is that they enhance security awareness through practical experiential, hands-on exercises such as this VIRTTX. These exercises bring together staff from across a business to evaluate existing IR/DR processes to determine if they are fit for purpose, establish existing gaps and identify strategies to prevent future threats, including during challenging circumstances such as the COVID-19 outbreak. Furthermore, the use of TTXs or TTEs for scenario-based incident response exercises was extremely useful for cyber security practice because well-crafted and well-executed exercises have been found to serve as valuable and effective tools for raising cyber security awareness among senior leadership, managers and IT professionals (Ulmanová, 2020). Originality/value This paper underlines the importance of practical, scenario-based cyber-IR training and reports on the experience of conducting a virtual IR/DR tabletop exercise within a large organisation.
Giddeon Njamngang Angafor, Iryna Yevseyeva, Leandros Maglaras
Inf. Comput. Secur.2
2023 Preface
Michael T. M. Emmerich, André H. Deutz, Iryna Yevseyeva
Nat. Comput.3
2022 CAESAR8: An agile enterprise architecture approach to managing information security risks
abstract
In theory, implementing an Enterprise Architecture (EA) should enable organizations to increase the accuracy of information security risk assessments. In reality, however, organizations struggle to fully implement EA frameworks because the requirements for implementing an EA and the benefits of commercial frameworks are unclear, and the overhead of maintaining EA artifacts is unacceptable, especially for smaller organizations. In this paper, we describe a novel approach called CAESAR8 (Continuous Agile Enterprise Security Architecture Review in 8 domains) that supports dynamic and holistic reviews of information security risks in IT projects. CAESAR8’s nonlinear design supports continuous reassessment of information security risks, based on a checklist that assesses the maturity of security considerations in eight domains that often cause information security failures. CAESAR8 assessments can be completed by multiple stakeholders independently, thus ensuring consideration of their tacit knowledge while preventing groupthink. Our evaluation with experienced industry professionals showed that CAESAR8 successfully addresses real-world problems in information security risk management, with significant benefits particularly for smaller organizations.
Paul Loft, Ying He 0004, Iryna Yevseyeva, Isabel Wagner
Comput. Secur.3
2021 Preface to the special issue dedicated to the 14th international workshop on global optimization held in Leiden, The Netherlands, September 18-21, 2018
André H. Deutz, Michael T. M. Emmerich, Yaroslav D. Sergeyev, Iryna Yevseyeva
J. Glob. Optim.4
2021 Designing Strong Privacy Metrics Suites Using Evolutionary Optimization
abstract
The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.
Isabel Wagner, Iryna Yevseyeva
ACM Trans. Priv. Secur.2
2019 Users Intention Based on Twitter Features Using Text Analytics
Qadri Mishael, Aladdin Ayesh, Iryna Yevseyeva
IDEAL (1)3
2019 Exploring the role of work identity and work locus of control in information security awareness
Lee Hadlington, Masa Popovac, Helge Janicke, Iryna Yevseyeva, Kevin I. Jones
Comput. Secur.4
2019 Improving the drug discovery process by using multiple classifier systems
David Ruano-Ordás, Iryna Yevseyeva, Vitor Basto-Fernandes, José Ramón Méndez 0001, Michael T. M. Emmerich
Expert Syst. Appl.2
2019 Published incidents and their proportions of human error
abstract
Purpose This paper aims to provide an understanding of the proportions of incidents that relate to human error. The information security field experiences a continuous stream of information security incidents and breaches, which are publicised by the media, public bodies and regulators. Despite the need for information security practices being recognised and in existence for some time, the underlying general information security affecting tasks and causes of these incidents and breaches are not consistently understood, particularly with regard to human error. Design/methodology/approach This paper analyses recent published incidents and breaches to establish the proportions of human error and where possible subsequently uses the HEART (human error assessment and reduction technique) human reliability analysis technique, which is established within the safety field. Findings This analysis provides an understanding of the proportions of incidents and breaches that relate to human error, as well as the common types of tasks that result in these incidents and breaches through adoption of methods applied within the safety field. Originality/value This research provides original contribution to knowledge through the analysis of recent public sector information security incidents and breaches to understand the proportions that relate to human error.
Mark Glenn Evans, Ying He 0004, Iryna Yevseyeva, Helge Janicke
Inf. Comput. Secur.3
2019 Application of portfolio optimization to drug discovery
Iryna Yevseyeva, Eelke B. Lenselink, Alice de Vries, Adriaan P. IJzerman, André H. Deutz, Michael T. M. Emmerich
Inf. Sci.1
2018 Multiobjective sparse ensemble learning by means of evolutionary algorithms
Jiaqi Zhao 0001, Licheng Jiao, Shixiong Xia, Vitor Basto-Fernandes, Iryna Yevseyeva, Yong Zhou 0003, Michael T. M. Emmerich
Decis. Support Syst.5
2017 Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms
Longmei Li, Iryna Yevseyeva, Vitor Basto-Fernandes, Heike Trautmann, Ning Jing, Michael T. M. Emmerich
EMO2
2017 Corrigendum to 'Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms' [Information Sciences volumes 367-368 (2016) 80-104]
Jiaqi Zhao 0001, Vitor Basto-Fernandes, Licheng Jiao, Iryna Yevseyeva, Asep Maulana, Rui Li 0001, Thomas Bäck, Ke Tang 0001, Michael T. M. Emmerich
Inf. Sci.4
2016 Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms
Jiaqi Zhao 0001, Vitor Basto-Fernandes, Licheng Jiao, Iryna Yevseyeva, Asep Maulana, Rui Li 0001, Thomas Bäck, Ke Tang 0001, Michael T. M. Emmerich
Inf. Sci.4
2016 Modeling and analysis of influence power for information security decisions
Iryna Yevseyeva, Charles Morisset, Aad P. A. van Moorsel
Perform. Evaluation1
2014 A Portfolio Optimization Approach to Selection in Multiobjective Evolutionary Algorithms
Iryna Yevseyeva, Andreia P. Guerreiro, Michael T. M. Emmerich, Carlos M. Fonseca
PPSN1
2013 Optimising anti-spam filters with evolutionary algorithms
Iryna Yevseyeva, Vitor Basto-Fernandes, David Ruano-Ordás, José Ramón Méndez 0001
Expert Syst. Appl.1