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Rethinking safety in autonomous ecosystems

Halasz David, Buhnova Barbora

Annals of Computer Science and Information Systems

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2022

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Vol. 31

81--87

EN

As autonomous cyber-physical systems are responding to the dynamism of our hyper-connected digital world, they are forming so called dynamic autonomous ecosystems, which require a change in methods ensuring their safe behavior. Within this change, reactions to predictable scenarios need to be replaced with adaptability to the unpredictable context, with gradual safety mechanisms, able to decide whether or not to trigger a certain mitigation procedure. In this paper, we outline our vision towards evolution of safety mechanisms to support dynamic and self-adaptive architectures of autonomous ecosystems. We are proposing an approach to address this research problem with the help of trust and reputation combined with gradual adaptation of safety procedures at runtime.

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An integrated checklist for architecture design of critical software systems

Bierska Adela, Buhnova Barbora, Bangui Hind

Annals of Computer Science and Information Systems

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2022

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Vol. 31

133-140

EN

With the advancement of digitalization, critical information infrastructures, such as intelligent energy distribution, transportation, or healthcare, have opened themselves towards intelligent technological opportunities, including automation of previously manual decision making. As a side effect, the digitalization of these infrastructures gives rise to new challenges, especially linked to the complexity of architecture design of these infrastructures, to later support necessary software quality and safeguard the systems against attacks and other harm. To support software architects in the design of these critical software systems, well structure architectural knowledge would be of great help to prevent the architects from missing some of the crucial concerns that need to be reflected with built-in architectural mechanisms, early during architecture design.Given the narrow scope of existing guidelines, with the need of browsing and combining multiple sources, this paper proposes an integrated checklist to cover the breath of architectural concerns for the design of critical software systems, covering the need for built-in mechanisms to prevent, detect, stop, recover from and analyse intentional as well as unintentional threats to system dependability. Contrary to existing guidelines that typically focus on runtime incident handling, our checklist is to be used during architecture design to ensure that the system has built-in mechanisms to either handle the incidents automatically or include the right mechanisms to support the runtime incident handling.

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Identification of Unintentional Perpetrator Attack Vectors using Simulation Game: A Case Study

Macák Martin, Bojnak Stefan, Buhnova Barbora

Annals of Computer Science and Information Systems

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2021

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Vol. 25

349--356

EN

In our digital era, insider attacks are among the serious underresearched areas of the cybersecurity landscape. A significant type of insider attack is facilitated by employees without malicious intent. They are called unintentional perpetrators. We proposed mitigating these threats using a simulation-game platform to detect the potential attack vectors. This paper introduces and implements a scenario that demonstrates the usability of this approach in a case study. This work also helps to understand players' behavior when they are not told upfront that they will be a target of social engineering attacks. Furthermore, we provide relevant acquired observations for future research.

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How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB

Macak Martin, Stovcik Matus, Buhnova Barbora, Merjavy Michal

Annals of Computer Science and Information Systems

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2020

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Vol. 21

463--470

EN

Digitalization is currently the key factor for progress, with a rising need for storing, collecting, and processing large amounts of data. In this context, NoSQL databases have become a popular storage solution, each specialized on a specific type of data. Next to that, the multi-model approach is designed to combine benefits from different types of databases, supporting several models for data. Despite its versatility, a multi-model database might not always be the best option, due to the risk of worse performance comparing to the single-model variants. It is hence crucial for software engineers to have access to benchmarks comparing the performance of multi-model and single-model variants. Moreover, in the current Big Data era, it is important to have cluster infrastructure considered within the benchmarks. In this paper, we aim to examine how the multi-model approach performs compared to its single-model variants. To this end, we compare the OrientDB multi-model database with the Neo4j graph database and the MongoDB document store. We do so in the cluster setup, to enhance state of the art in database benchmarks, which is not yet giving much insight into cluster-operating database performance.