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Ekstedt, M., Afzal, Z., Mukherjee, P., Hacks, S. & Lagerström, R. (2023). Yet another cybersecurity risk assessment framework. International Journal of Information Security, 22(6), 1713-1729
Open this publication in new window or tab >>Yet another cybersecurity risk assessment framework
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2023 (English)In: International Journal of Information Security, ISSN 1615-5262, E-ISSN 1615-5270, Vol. 22, no 6, p. 1713-1729Article in journal (Refereed) Published
Abstract [en]

IT systems pervade our society more and more, and we become heavily dependent on them. At the same time, these systems are increasingly targeted in cyberattacks, making us vulnerable. Enterprise and cybersecurity responsibles face the problem of defining techniques that raise the level of security. They need to decide which mechanism provides the most efficient defense with limited resources. Basically, the risks need to be assessed to determine the best cost-to-benefit ratio. One way to achieve this is through threat modeling; however, threat modeling is not commonly used in the enterprise IT risk domain. Furthermore, the existing threat modeling methods have shortcomings. This paper introduces a metamodel-based approach named Yet Another Cybersecurity Risk Assessment Framework (Yacraf). Yacraf aims to enable comprehensive risk assessment for organizations with more decision support. The paper includes a risk calculation formalization and also an example showing how an organization can use and benefit from Yacraf.

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
Attack tree, Enterprise IT risk, Risk assessment, Threat modeling
National Category
Computer Sciences Computer Systems
Identifiers
urn:nbn:se:kth:diva-338542 (URN)10.1007/s10207-023-00713-y (DOI)001027329600001 ()2-s2.0-85164669184 (Scopus ID)
Note

QC 20231108

Available from: 2023-11-08 Created: 2023-11-08 Last updated: 2023-11-08Bibliographically approved
Fahlander, P., Ekstedt, M., Mukherjee, P. & Dwivedi, A. K. (2022). Containment Strategy Formalism in a Probabilistic Threat Modelling Framework. In: Paolo Mori, Gabriele Lenzini, Steven Furnell (Ed.), Proceedings of the 8th international conference on information systems security and privacy (ICISSP): . Paper presented at 8th International Conference on Information Systems Security and Privacy (ICISSP), Virtual/Online, 9-11 February, 2022 (pp. 108-120). Scitepress, 1
Open this publication in new window or tab >>Containment Strategy Formalism in a Probabilistic Threat Modelling Framework
2022 (English)In: Proceedings of the 8th international conference on information systems security and privacy (ICISSP) / [ed] Paolo Mori, Gabriele Lenzini, Steven Furnell, Scitepress , 2022, Vol. 1, p. 108-120Conference paper, Published paper (Refereed)
Abstract [en]

Foreseeing, mitigating and preventing cyber-attacks is more important than ever before. Advances in the field of probabilistic threat modelling can help organisations understand their own resilience profile against cyber-attacks. Previous research has proposed MAL, a meta language for capturing the attack logic of a considered domain and running attack simulations in a model that depicts the defended IT-infrastructure. While this modality is already somewhat established for proposing general threat mitigation actions, less is known about how to model containment strategies in the event that penetration already has occurred. The problem is a fundamental gap between predominant threat models in cyber-security research and containment in the incident response lifecycle. This paper presents a solution to the problem by summarizing a methodology for reasoning about containment strategies in MAL-based threat models.

Place, publisher, year, edition, pages
Scitepress, 2022
Series
Proceedings of the 8th International Conference on Information Systems Security and Privacy 2022, ISSN 2184-4356
Keywords
Threat Analysis, MAL, Containment strategies, Simulated Annealing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-310910 (URN)10.5220/0010823800003120 (DOI)000818770500009 ()2-s2.0-85176317924 (Scopus ID)
Conference
8th International Conference on Information Systems Security and Privacy (ICISSP), Virtual/Online, 9-11 February, 2022
Projects
SOCCRATES
Funder
Security Link, 833481
Note

Part of proceedings: ISBN 978-989-758-553-1

QC 20220419

QC 20220708

Available from: 2022-04-11 Created: 2022-04-11 Last updated: 2023-11-23Bibliographically approved
Widel, W., Mukherjee, P. & Ekstedt, M. (2022). Security Countermeasures Selection Using the Meta Attack Language and Probabilistic Attack Graphs. IEEE Access, 10, 89645-89662
Open this publication in new window or tab >>Security Countermeasures Selection Using the Meta Attack Language and Probabilistic Attack Graphs
2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 89645-89662Article in journal (Refereed) Published
Abstract [en]

Connecting critical infrastructure assets to the network is absolutely essential for modern industries. In contrast to the apparent advantages, network connectivity exposes other infrastructure vulnerabilities that can be exploited by attackers. To protect the infrastructure, precise countermeasure identification is necessary. In this regard, the objective for the security officers is to identify the optimal set of countermeasures under a variety of budgetary restrictions. Our approach is based on the Meta Attack Language framework, which allows for convenient modelling of said infrastructures, as well as for automatic generation of attack graphs describing attacks against them. We formalize the problem of the selection of countermeasures in this context. The formalization makes it possible to deal with an arbitrary number of budgets, expressing available resources of both monetary and time-like nature, and to model numerous dependencies between countermeasures, including order dependencies, mutual exclusivity, and interdependent implementation costs. We propose a flexible and scalable algorithm for the problem. The whole methodology is validated in practice on realistic models.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-316948 (URN)10.1109/access.2022.3200601 (DOI)000848230900001 ()2-s2.0-85137583573 (Scopus ID)
Note

QC 20220920

Available from: 2022-09-01 Created: 2022-09-01 Last updated: 2022-09-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2549-6578

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