E.V. Zelenko, postgraduate student
Cherkasy State Technological University
460 Shevchenko Blvd., Cherkasy, 18006, Ukraine
This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2024, 46(2):03-14

https://doi.org/10.15407/emodel.46.02.003

ABSTRACT

Reviewed: features of the definition of an agent and a software agent, its dimensions and other components; models of software agents and its properties; classification of software agents by architecture, communication principles and agent communication languages (ACL), as well as existing platforms for their development (e.g., JADE, SPADE); multi-agent system (MAS); behavior types of SPADE software agent based on the example of one of the platforms (including for subsequent experiments to compare behaviors in terms of hardware resources usage).

Minor adjustments have been made to the syntax of mathematical expressions describing the agent model, and a revision of the formalized definitions of agent property set has been proposed; a formalized description of the model of studied agent type is determined.

KEYWORDS

behavior, spade, jade, mas, aose, aop, bdi.

REFERENCES

1. Slhoub, Khaled Ali M. (2018). Standardizing the Requirements Specification of Multi-Agent Systems [Dissertation of PhD in Computer Science, Florida Institute of Technology]. Scholarship repository. https://repository.fit.edu/etd/910/
2. Kirrane, S. (2021). Intelligent software web agents: A gap analysis. Journal of Web Semantics, 71, 100659. https://doi.org/10.1016/j.websem.2021.100659
3. Abbasi, K.M., Khan, T.A., & Irfan ul Haq. (2022). Framework for Integrated Use of Agent-Based and Ambient-Oriented Modeling. Mathematics (Basel), 10(21), 4157. 
   https://doi.org/10.3390/math10214157
4. Oyelami,O., & Olivier, M. (2017). Establishing Findings in Digital Forensic Examinations: A Case Study Method. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics XIII. DigitalForensics 2017. IFIP Advances in Information and Communication Technology, 511. Springer, Cham. 
   https://doi.org/10.1007/978-3-319-67208-3_1
5. Ubuntu documentation. (2024, February 1^st). CronHowto. https://help.ubuntu.com/community/CronHowto
6. Love, R. (2013). Linux System Programming: Talking Directly to the Kernel and C Library (2^nd ed.). O'Reilly Media. ISBN: 978-1-449-33953-1.
7. Wooldridge, M.J. (2002). An Introduction to Multiagent Systems (1^st ed.). New York: John Wiley & Sons. ISBN-10: 047149691X.
8. Bădică, C., Budimac, Z., Burkhard, H.D., & Ivanovic, M. (2011). Software agents: Languages, tools, platforms. Computer Science and Information Systems, 8(2), 255-298. 
   https://doi.org/10.2298/CSIS110214013B
9. GitHub. (n.d.). Spade-BDI. https://github.com/javipalanca/spade_bdi
10. Pal, C.V., Leon, F., & Ganzha, M. (2020). A Review of Platforms for the Development of Agent Systems. arXiv. Multiagent Systems (cs.MA). https://doi.org/10.48550/arXiv. 2007.08961
11. Dennis, L.A., & Oren, N. (2022). Explaining BDI agent behaviour through dialogue. Auton Agent Multi-Agent Syst, 36(29).
    https://doi.org/10.1007/s10458-022-09556-8
12. Holgado-Terriza, J.A., Pico-Valencia, P., & Garach-Hinojosa, A. (2020). A Gateway for Enabling Uniform Communication Among Inter-Platform JADE Agents. IOS Press, Intelligent Environments, 28, 82-91. https://doi.org/10.3233/AISE200027
13. Negre, E., Arru, M., & Rosenthal-Sabroux, C. (2018). 7 — Toward a Modeling of Population Behaviors in Crisis Situations. In Sèdes, F. (Eds.), How Information Systems Can Help in Alarm/Alert Detection, Elsevier. (pp. 199-218). ISTE Press. 
    https://doi.org/10.1016/B978-1-78548-302-8.50007-1
14. Teahan, W.J. (2010). Artificial Intelligence — Agent Behaviour. Bookboon. ISBN: 9788776815592.
15. Ajith,J.E., Selvaraj, S., Aadhavan, J, & Rajkumar, S. (2016). Revolution in e-commerce by the usage of software agents. International Journal of Advanced Computing and Electronics Technology (IJACET), 3(5).
16. Jeon, Y.A. (2022). Let me transfer you to our AI-based manager: Impact of manager-level job titles assigned to AI-based agents on marketing outcomes. Journal of Business Research, 145, 892-904. 
    https://doi.org/10.1016/j.jbusres.2022.03.028
17. Kim, J. (2020). The influence of perceived costs and perceived benefits on AI-driven interactive recommendation agent value. Journal of Global Scholars of Marketing Science, 30(3), 319-333. 
    https://doi.org/10.1080/21639159.2020.1775491
18. Hauptman, A.I., Schelble, B.G., & McNeese, N.J. (2023). Adapt and overcome: Perceptions of adaptive autonomous agents for human-AI teaming. Computers in Human Behavior, 138, 107451. 
    https://doi.org/10.1016/j.chb.2022.107451
19. Bartram, S.M., Branke, J., & Motahari, M. (2020). Artificial intelligence in asset management. CFA Institute Research Foundation. ISBN 978-1-952927-02-7.
    https://doi.org/10.2139/ssrn.3692805
20. Raisinghani, M.S., Klassen, C., & Schkade, L.L. (2009). Intelligent Software Agents in E-Commerce. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology (2^nd ed., pp. 2137-2140). IGI Global. 
    https://doi.org/10.4018/978-1-60566-026-4.ch336
21. Yu, L., Masabo, E., Tan, L., & He, M. (2008). Multi-Agent Automated Intelligent Shopping System (MAISS). The 9^th International Conference for Young Computer Scientists (pp. 665-670). Hunan, China. 
    https://doi.org/10.1109/ICYCS.2008.35
22. Corradi, A., Cremonini, M., Montanari, R., & Stefanelli, C. (1999). Mobile agents integrity for electronic commerce applications. Information Systems, 24(6), 519-533. 
    https://doi.org/10.1016/S0306-4379(99)00030-7
23. Borysov, S.S., Rich, J., & Pereira, F.C. (2019). How to generate micro-agents? A deep generative modeling approach to population synthesis. Transportation Research Part C: Emerging Technologies, 106, 73-97. 
    https://doi.org/10.1016/j.trc.2019.07.006
24. German, E., & Sheremetov, L. (2007). Specifying Interaction Space Components in a FIPA-ACL Interaction Framework. In F. Sèdes (Eds.), Languages, Methodologies and Development Tools for Multi-Agent Systems (LADS) (pp. 191-208). Springer, Berlin, Heidelberg. 
    https://doi.org/10.1007/978-3-540-85058-8_12
25. Pandey, A.K., & Saxena, A.S. (2016). Underpinning of Object Oriented Software Agent in portalware. International Journal of Computer Science & Engineering Technology (IJCSET), 7(6), 292-295.
26. Abbas, H., Shaheen, S., & Amin, M. (2018). Providing a transparent dynamic organization technique for efficient aggregation of multiple JADE agent platforms. International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan (pp. 100-108). 
    https://doi.org/10.1109/ITCE.2018.8316607
27. Ajitha, S., Mithun, G., & Suresh Kumar, T.V. (2016). Optimal travel management using software agent. International Conference on Circuits, Controls, Communications and Computing (I4C), Bangalore, India (pp. 1-4). 
    https://doi.org/10.1109/CIMCA.2016.8053289
28. Sheremetov, L.B., Martínez, J., & Guerra, J. (2003). Agent Architecture for Dynamic Job Routing in Holonic Environment Based on the Theory of Constraints. 1^st International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS) (pp. 124- 133). Springer, Berlin, Heidelberg. 
    https://doi.org/10.1007/978-3-540-45185-3_12
29. Palanca, J., Terrasa, A., Julian, V., & Carrascosa, C. (2020). SPADE 3: Supporting the New Generation of Multi-Agent Systems. IEEE Access, 8, 182537-182549.
    https://doi.org/10.1109/ACCESS.2020.3027357
30. Donancio, H., Casals, A., & Brandão, A.A. (2019). Exposing agents as web services: a case study using JADE and SPADE. Anais do XV Workshop-Escola de Sistemas de Agentes, seus Ambientes e apliCações, WESAAC 2019 (pp. 131-142).
31. Python.org. (n.d.). https://www.python.org
32. Spade-BDI. (n.d.). Spade-BDI. https://spade-bdi.readthedocs.io/en/latest/readme.html
33. PyPI.org. (n.d.). spade 3.3.2. https://pypi.org/project/spade/
34. Palanca, J. (2018). SPADE Documentation. https://buildmedia.readthedocs.org/media/pdf/ spade-mas/feature-3.0/spade-mas.pdf
35. SPADE. (n.d.). SPADE. https://spade-mas.readthedocs.io/en/develop/readme.html
36. Lyu, G., Fazlirad, A., & Brennan, R.W. (2020). Multi-Agent Modeling of Cyber-Physical Systems for IEC 61499 Based Distributed Automation. Procedia Manufacturing, 51, 1200-1206. 
    https://doi.org/10.1016/j.promfg.2020.10.168
37. Palanca, J., Rincon, J.A., Carrascosa, C., Julian, V.J., & Terrasa, A. (2023). Flexible Agent Architecture: Mixing Reactive and Deliberative Behaviors in SPADE. Electronics, 12(3), 659. 
    https://doi.org/10.3390/electronics12030659
38. Frayle Pérez, S. (2023). Spade-BDI Documentation. https://spade-bdi.readthedocs.io/_/ downloads/en/latest/pdf/
39. Palanca, J., Rincon, J.A., Carrascosa, C., Julian, V., & Terrasa, A. (2022). A Flexible Agent Architecture in SPADE. Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation: The PAAMS Collection, 20^th International Conference, 13616 (pp. 320-331). 
    https://doi.org/10.1007/978-3-031-18192-4_26
40. SPADE. (n.d.). The SPADE agent model. https://spade-mas.readthedocs.io/en/latest/model.html
41. SPADE. (n.d.) Advanced Behaviours. https://spade-mas.readthedocs.io/en/latest/beha­viours.html

Full text: PDF

A.I. Krasilnikov

Èlektron. model. 2024, 46(2):15-34

https://doi.org/10.15407/emodel.46.02.015

ABSTRACT

The dependence of the extremes and zeros of the excess kurtosis  on the weight coefficient  is researched. Formulas for finding the extrema points, the values of the minimums and maximums of the excess kurtosis are obtained. Conditions on the shift parameter  under which the extrema points belong to the interval  are determined. Formulas for finding the zeros of the excess kurtosis are obtained and conditions on shift parameter under which the roots of the equation  are real and belong to the interval  are determined. Examples of calculating extremes and zeros of the excess kurtosis of two-component mixtures of shifted non-Gaussian distributions are considered. The results of the research justify the possibility of practical application of two-component mixtures of shifted distributions for mathematical and computer modeling of an infinite number of non-Gaussian random variables with negative, positive and zero excess kurtosis.

KEYWORDS

non-Gaussian distributions, two-component mixtures of distributions, cumulant analysis, cumulant coefficients, skewness, excess kurtosis.

REFERENCES

1. Titterington, D.M., Smith, A.F.M. & Makov, U.E. (1985). Statistical analysis of finite mixture distributions. New York: John Wiley & Sons.
2. McLachlan, G. & Peel, D. (2000). Finite mixture models. New York: John Wiley & Sons.
   https://doi.org/10.1002/0471721182
3. Korolev, V.Yu. (2004). Smeshannye gaussovskie veroiatnostnye modeli realnykh protses­sov [The Mixed Gaussian Probabilistic Models of Real Processes]. Moscow: Maks Press. (in Russian).
4. Aprausheva, N.N. & Sorokin, S.V. (2015). Zametki o gaussovykh smesiakh [Notes on Gaussian mixtures]. Moscow: VTs Rossiiskoi akade­mii nauk. (in Russian).
5. Tukey, J.W. (1960). A survey of sampling from contaminated distributions. In I. Olkin (ed.) Contributions to Probability and Statistics (pp. 448-485). Stanford: Stanford Univ. Press.
6. Shin, J.-Y., Lee, T. & Ouarda, T.B.M.J. (2015). Heterogeneous Mixture Distributions for Modeling Multisource Extreme Rainfalls. Journal of hydrometeorology, 16(6), 2639- 
   https://doi.org/10.1175/JHM-D-14-0130.1
7. Türkan, A.H. & Çalış, N. (2014). Comparison of two-component mixture distribution models for heterogeneous survival datasets: a review study. ISTATISTIK: Journal of the Turkish Statistical Association, 7(2), 33-
8. Uma maheswari, R. & Leo Alexander, T. (2017). Two-component of Non-Identical Mixture Distribution Models for heterogeneous Survival Data. International Journal of Recent Scientific Research, 8(10), 20813-
9. Krasilnikov, A.I. & Pilipenko, K.P. (2008). Application of a two-component Gaussian mixture to identify single-peak symmetric probability density functions. Elektronika i sviaz, 5 (46), 20- (in Russian).
10. Chauveau, D., Garel, B. & Mercier, S. (2019). Testing for univariate two-component Gaussian mixture in practice. Journal de la société française de statistique, 160(1), 86-113
11. Kalantan, Z.I. & Alrewely, F. (2019). A 2-Component Laplace Mixture Model: Properties and Parametric Estimations. Mathematics and Statistics, 7(4A), 9–16. 
    https://doi.org/10.13189/ms.2019.070702
12. Sindhu, T.N., Feroze, N. & Aslam, M. (2014). Bayesian Estimation of the Parameters of Two-Component Mixture of Rayleigh Distribution under Doubly Censoring. Journal of Modern Applied Statistical Methods, 13(2), 259–286. 
    https://doi.org/10.22237/jmasm/1414815180
13. Evin, G., Merleau, J. & Perreault, L. (2011). Two-component mixtures of normal, gamma, and Gumbel distributions for hydrological applications. Water Resources Research, 47(W08525), 21 p. 
    https://doi.org/10.1029/2010WR010266
14. Uma maheswari, R. & Leo Alexander, T. (2017). Mixture of identical distributions of exponential, gamma, lognormal, weibull, gompertz approach to heterogeneous survival data. International Journal of Current Research, 9(09), 57521-57532.
15. Krasilnikov, A.I. & Pilipenko, K.P. (2007). Unimodal two-componental Gaussian mixture. Excess kurtosis. Elektronika i sviaz, 2 (37), 32-38 (in Russian).
16. Krasilnikov, A.I. (2017). Analysis of the Kurtosis Coefficient of Contaminated Gaussian Distributions. Elektronnoe modelirovanie, 39(4), 19- 
    https://doi.org/10.15407/emodel.39.04.019
17. Krasil’nikov, A.I. (2013). Class non-Gaussian distributions with zero skewness and kurtosis. Radioelectronics and Communications Systems, 56(6), 312- 
    https://doi.org/10.3103/S0735272713060071
18. Krasilnikov, A.I. (2017). Class of Non-Gaussian Symmetric Distributions with Zero Coefficient of Elektronnoe modelirovanie, 39(1), 3-17. 
    https://doi.org/10.15407/emodel.39.01.003
19. Barakat, H. M. (2015). A new method for adding two parameters to a family of distributions with application to the normal and exponential families. Statistical Methods & Applications, 24(3), 359-372 
    https://doi.org/10.1007/s10260-014-0265-8
20. Barakat, H.M., Aboutahoun, A.W. & El-kadar, N.N. (2019). A New Extended Mixture Skew Normal Distribution, With Applications. Revista Colombiana de Estadstica, 42(2), 167- 
    https://doi.org/10.15446/rce.v42n2.70087
21. Sulewski, P. (2021). Two-piece power normal distribution. Communications in Statistics — Theory and Methods, 50(11), 2619–2639. 
    https://doi.org/10.1080/03610926.2019.1674871
22. Kunchenko, Yu.P. (2001). Polinomialnyie otsenki parametrov blizkikh k gaussovskim sluchainykh velichin. Ch. 1. Stokhasticheskie polinomy, ikh svoistva i primeneniya dlya nakhozhdeniya otsenok parametrov [Polynomial Parameter Estimations of Close to Gaussian Random variables. P. 1. Stochastic Polynomials, Their Properties and Applications for Finding Parameter Estimations]. Cherkassy: ChITI. (in Russian).
23. Krasilnikov, A.I. (2018). Modeling of Perforated Random Variables on the Basis of Mixtures of Shifted Distributions. Elektronnoe modelirovanie, 40(1), 47-61 
    https://doi.org/10.15407/emodel.40.01.047
24. Krasilnikov, A.I. (2018). The Application of Two-Component Mixtures of Shifted Distributions for Modeling Perforated Random Variables. Elektronnoe modelirovanie, 40(6), 83-98 
    https://doi.org/10.15407/emodel.40.06.083
25. Krasilnikov, A.I. (2020). Analysis of Cumulant Coefficients of Two-component Mixtures of Shifted Gaussian Distributions with Equal Variances. Elektronnoe modelirovanie, 42(3), 71-88 
    https://doi.org/10.15407/emodel.42.03.071
26. Krasylnikov, O.I. (2021). Analysis of Cumulant Coefficients of Two-Component Mixtures of Shifted Non-Gaussian Distributions. Elektronne modeliuvannia, 43(5), 73-92. 
    https://doi.org/10.15407/emodel.43.05.073

Full text: PDF

F.O. Korobeynikov, Ph.D. student
G.E. Pukhov Institute for Modelling in Energy Engineering
National Academy of Sciences of Ukraine
Ukraine, 03164, Kyiv, Str. General Naumov 15
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2024, 46(2):35-42

https://doi.org/10.15407/emodel.46.02.035

ABSTRACT

This research presents a three-dimensional risk matrix model designed for the analysis and prioritisation of critical risks in the context of resilience. Traditional risk assessment methods prevalent in information security, which typically juxtapose the likelihood and consequences of risks, are inadequate for fully capturing the intricacies of critical risks. The proposed three-dimensional model addresses these shortcomings by cohesively integrating the dimensions of likelihood, impact and cost of risk management. This integration provides a holistic tool for resilient risk analysis that goes beyond the capabilities of traditional models.

A key feature of this model is its ability to address the complexities associated with critical risks, which are often not adequately addressed by traditional risk matrices due to their stochastic nature and significant potential impact on organisational resilience. By incorporating budgetary constraints into the risk assessment process, the model enables a more objective and quantifiable approach to managing critical risks. It shifts the evaluative focus from a purely probabilistic perspective to a cost-value based assessment, emphasising the balance between potential benefits and mitigation expenditure.

This approach not only refines the accuracy of critical risk assessment, but also enhances existing risk management practices, providing a more robust and strategic tool for managing organisational risk.

KEYWORDS

Risk Management, Resilience, Risk Matrix, Information Security, Critical Risk Analysis, Stochastic HILP Risks.

REFERENCES

1. Mokhor, V., Bakalynskyi, O., & Tsurkan, V. (2018). Risk assessment presentation of information security by the risks map. Collection "Information technology and security", 6(2), 94—104. 
   https://doi.org/10.20535/2411-1031.2018.6.2.153494
2. Hobbs, K.L., Lyons, J.B., Feather, M.S., Bycroft, B.P., Phillips, S., Simon, M., Harter, M., Costello, K., Gawdiak, Y., & Paine, S. (2023). Space Trusted Autonomy Readiness Le­vels. In 2023 IEEE Aerospace Conference. IEEE. 
   https://doi.org/10.1109/AERO55745.2023.10115976
3. Li, Z.P., Yee, Q.M.G., Tan, P.S., & Lee, S.G. (2013). An extended risk matrix approach for supply chain risk assessment. In 2013 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). 
   https://doi.org/10.1109/IEEM.2013.6962700
4. Vaezi, A., Jones, S., & Asgary, A. (2024). Integrating Resilience into Risk Matrices: A Practical Approach to Risk Assessment with Empirical Analysis. Journal of Risk Analysis and Crisis Response, 13(4). 
   https://doi.org/10.54560/jracr.v13i4.411
5. Korobeynikov F. Resilience Paradigm Development in The Security Domain. Electronic Modeling. 2023. Vol. 45, no. 4. P. 88—111. URL: 
   https://doi.org/10.15407/emodel.45.04.088

Full text: PDF

A. Podzolkov, V. Kharchenko

Èlektron. model. 2024, 46(2):43-59

https://doi.org/10.15407/emodel.46.02.043

ABSTRACT

The methods of assessing the security of information systems (IS) with the help of special means of penetration testing (PT) and services that provide the corresponding tools (Penetration Testing as a Service, PTaaS) are analyzed. The indicators to compare PTaaS tools and services are substantiated, namely: provision of a report on compliance of the tested product with data protection requirements, availability of security certificates, use of appropriate testing methodologies, etc. A method has been developed for selecting a PTaaS service according to the customer’s requirements to increase IS cyber security by improving the completeness and reliability of penetration testing, as well as reducing the search time for PT tools. A cloud service is proposed that supports the implementation of the method and provides the option of choosing PTaaS. It was determined that the use of the proposed method and service enables users to quickly and conveniently choose PTaaS according to the requirements and work model of organizations or digital products.

KEYWORDS

cybersecurity, penetration testing, penetration testing as a service, data security, PTaaS choice.

REFERENCES

1. IBM. (2023). Cost of a Data Breach Report 2023. https://www.ibm.com/downloads/cas/E3G5JMBP
2. Dalalana Bertoglio, D., Zorzo, A. (2017). Overview and open issues on penetration test. J Braz Comput Soc, 23(2). 
   https://doi.org/10.1186/s13173-017-0051-1
3. Aileen G,B., Xiaohong, Y., Bei, T., Bill, C., Monique, J. (2011). An Overview of Penetration Testing. International Journal of Network Security & Its Applications, 3(6), 19-38. 
   https://doi.org/10.5121/ijnsa.2011.3602
4. Ralph, L., Thomas, M. (2012). Сloud penetration testing. International Journal on Cloud Computing: Services and Architecture (IJCCSA), 2(6). 
   https://doi.org/10.5121/ijccsa.2012.2604
5. Altulaihan, E.A., Alismail, A., Frikha, M. (2023). A Survey on Web Application Penetration Testing. Electronics, 12(5). 
   https://doi.org/10.3390/electronics12051229
6. (n.d.). OWASP Testing Guide. https://owasp.org/www-project-web-security-testing-guide/v42/
7. (2017). CREST Penetration Testing Guide. https://www.crest-approved.org/wp-content/uploads/2022/04/CREST-Penetration-Testing-Guide-1.pdf
8. Li, Y., Wang, Y., Xiong, X., Zhang, J., Yao, Q. (2022). An Intelligent Penetration Test Simulation Environment Construction Method Incorporating Social Engineering Factors. Applied Sciences. 12(12). 
   https://doi.org/10.3390/app12126186
9. Ghanem, M.C., Chen, T.M. (2020). Reinforcement Learning for Efficient Network Penetration Testing. 11(6). 
   https://doi.org/10.3390/info11010006
10. Chenxi, W. (2022). The PtaaS Book: The A-Z of Pentest as a Service. AimPoint Group, LLC.
11. Software Testing Help. (2024). Top 10 Pen Testing as a Service (PTaaS) Providers in 2024. https://www.softwaretestinghelp.com/top-pen-testing-as-a-service-providers/
12. Podzolkov, A.V. (n.d.) Penetration testing service suggestion tool. https://leftchameleon.bubbleapps.io/version-test
13. Abakumov, A.I., Kharchenko, V.S. (2023). Combining Experimental and Analytical Methods for Penetration Testing of AI-Powered Robotic Systems. COLINS-2023: 7th International Conference on Computational Linguistics and Intelligent Systems. National Aerospace University «Kharkiv Aviation Institute». https://ceur-ws.org/Vol-3403/paper40.pdf
14. Tarasyuk, O.M., Kharchenko, V.S. (2003). Dynamic radial metric diagrams in software quality management problems. Collection of scientific works to G.E. Pukhov Institute of Modeling Problems in Energy. (22), 202-205.
15. Abakumov, A.I., Kharchenko V.S. (2023). Analytical and Experimental Methods for assessing safety and cybersecurity robotic systems. Methods and technologies for providing quality and safety of intelligent systems. Yuston.

Full text: PDF