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4 Annals of Computer Science and Information Systems

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

100%

Macák M. , Bojnak S. , Buhnova B.

tom Vol. 25

349--356

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.

A design and experiment of automation management system for platform as a service

84%

Tashkandi A.

2019

tom Vol. 18

711--715

Security [11] and quality [4] of cloud computing services represent significant factors that affect the adoption by consumers. Platform as a Service (PaaS) is one of cloud computing service models [14]. Management of database systems, middleware and application runtime environments is automated in PaaS [2]. PaaS automation management issues and requirements were collected in three rounds from information technology experts using Delphi technique. In this paper, PaaS automation quality and security management system is proposed and evaluated. Evaluation of the management system was based on experiment in a private cloud for an organization undergoing a transformation toward PaaS computing.

Big data platform for smart grids power consumption anomaly detection

67%

Lipcàk P. , Macak M. , Rossi B.

2019

tom Vol. 18

771--780

Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).

Formalization of software risk assessment results in legal metrology based on ISO/IEC 18045 vulnerability analysis

59%

Esche M. , Salwiczek F. , Grasso Toro F.

2019

tom Vol. 18

443--447

The Measuring Instruments Directive sets down essential requirements for measuring instruments subject to legal control in the EU. It dictates that a risk assessment must be performed before such instruments are put on the market. Because of the increasing importance of software in measuring instruments, a specifically tailored software risk assessment method has been previously developed and published. Related research has been done on graphical representation of threats by attack probability trees. The final stage is to formalize the method to prove its reproducibility and resilience against the complexity of future instruments. To this end, an inter-institutional comparison of the method is currently being conducted across national metrology institutes, while the weighing equipment manufacturers' association CECIP has provided a new measuring instrument concept, as a significant example of complex instruments. Based on the results of the comparison, a template to formalize the software risk assessment method is proposed here.