Using data from open sources for decision-making under information warfare
A review of methods, means, and tools that make it possible to use data from open sources for decision support in weakly-structured subject domains is presented. We show that presently it is impossible to completely replace expert data with data from open sources in the process of decision-making. Selection of experts and organization of expert examination require considerable time and funds. However, due to insufficient level of natural language processing technology development, the need to involve experts and knowledge engineers in the process of decision-making remains (even though significant role in this process is already played by data from open sources). Information, obtained from both experts and open sources, is processed, aggregated, and used as the basis to provide recommendations for the decision-maker regarding the selection of some particular decision variant or planning of further actions in general. As an example of a weakly-structured subject domain, we consider the sphere of information warfare, particularly focusing on information operation detection. For this domain we propose a hybrid decision support methodology, using both expert data and data from open sources. The methodology is based on the hierarchic decomposition of the main goal of information operation. The goal is, usually, to change the opinion of the target audience about the information operation object. Based on data obtained from experts and open sources, the knowledge base of the subject domain is built in the form of a weighted graph. It represents a hierarchy of factors that influence the main goal. Beside numeric value, the impact of each sub-goal in the graph is also characterized by certain delay and duration. With these parameters taken into consideration, the degree of main goal achievement is calculated, and changes of target parameters of information operation object are monitored. Usage of the proposed methodology is demonstrated on the example of detection and analysis actions intended to discredit the National academy of sciences of Ukraine. For this purpose, automated decision support and content monitoring tools are used
Full Text:PDF (Українська)
A. Dodonov, D. Lande, V. Tsyganok, O. Andriichuk, S. Kadenko, and A. Graivoronskaya, Information operation recognition. Kyiv, Ukraine: OOO “Inzhiniring”, 2017.
T. Taran, and D. Zubov, Artificial intelligence. Theory and applications. Luhansk, Ukraine: WNU of V.Dal’, 2006.
V. Tsyganok, S. Kadenko, O.Andriichuk, and P. Roik, “Usage of multicriteria decision-making support arsenal for strategic planning in environmental protection sphere,” Journal of Multi-criteria Decision Analysis, vol. 24, iss. 5-6, pp. 227-238, 2017. doi: 10.1002/mcda.1616.
V. Tsyganok, S. Kadenko, and O. Andriichuk, “Using Different Pair-wise Comparison Scales for Developing Industrial Strategies,” Int. J. Management and Decision Making, vol. 14, no. 3, pp. 224–250, 2015. doi: 10.1504/IJMDM.2015.070760.
V. Gorbulin, A. Dodonov, and D. Lande, Information operations and public security: threats, counteraction, modeling: a monograph. Kyiv, Ukraine: Internettechnologia, 2009.
S. Kadenko, “Prospects and Potential of Expert Decision-making Support Techniques Implementation in Information Security Area,” in CEUR Workshop Proceedings, Vol. 1813, pp. 8-14, 2016.
I. Kalpokas, “Information Warfare on Social Media: A Brand Management Perspective,” Baltic Journal of Law & Politics, vol. 10, iss. 1, pp. 35-62, 2017. doi: 10.1515/bjlp-2017-0002.
C. Wagnsson, and M. Hellman, “Normative Power Europe Caving In? EU under Pressure of Russian Information Warfare,” Journal of Common Market Studies, vol. 56, iss. 5, pp. 1161-1177, 2018. doi: 10.1111/jcms.12726.
A. Bohdanov, “Information component of hybrid war,” Information Technology and Security, vol. 3, iss. 1, pp. 25-30, 2015.
C. McCue, Data Mining and Predictive Analysis. Oxford, United Kingdom: Butterworth–Heinemann, 2015. doi: 10.1016/C2013-0-00434-3
M. Shafiq, X. Yu, and A. Laghari, “WeChat traffic classification using machine learning algorithms and comparative analysis of datasets,” International Journal of Information and Computer Security, vol. 10, iss. 2/3, pp.109-128. 2018. doi: 10.1504/IJICS.2018.091467.
J. Zhao, and H. Wang, “Detecting fake reviews via dynamic multimode network,” International Journal of High Performance Computing and Networking, vol. 13, no.4, pp. 408-416, 2019. doi: 10.1504/IJHPCN.2019.099264.
F. De Felice, A. Petrillo, and T.Saaty, Applications and Theory of Analytic Hierarchy Process. Decision Making for Strategic Decisions. London, United Kingdom: IntechOpen, 2016. doi: 10.5772/61387.
H. Q. Vu, G. Beliakov, and G. Li, “A Choquet Integral Toolbox and Its Application in Customer Preference Analysis,” in Data Mining Applications with R, Y. Zhao, and J. Cen Eds., Amsterdam, Nederland: Elsevier, 2014. pp. 247-272. doi: 10.1016/B978-0-12-411511-8.00009-8.
ATP 2-22.9. Open Source Intelligence. [Online]. Available: https://fas.org/irp/doddir/army/atp2-22-9.pdf. Accessed on: Febr. 19, 2019.
A. Tuzovsky, S. Chirikov, and V. Yampolsky, Knowledge management systems (methods and technologies). Tomsk, Russia: NTL Publishing, 2005.
S. Kadenko, “Problems of expert data representation in decision support systems”, Data recording, storage, and processing, vol. 18, no. 3, pp. 67-74, 2016.
S. Kadenko, “Defining Relative Weights of Data Sources during Aggregation of Pair-wise Comparisons,” Selected Papers of the XVII International Scientific and Practical Conference on Information Technologies and Security, 2017, pp. 47-55. [Online], Available: http://ceur-ws.org/Vol-2067/. Accessed on: Febr. 19, 2019.
C. Dwork, R. Kumar, M. Naor, and D. Sivakumar, “Rank aggregation methods for the Web,” in Proc. of the 10th international conference on World Wide Web, pp. 613-622, 2001. doi: 10.1145/371920.372165.
A. Grigoriev, D. Lande, S. Borodenkov, R. Mazurkevich, and V. Patsiora, InfoStream. Monitoring of news from the Internet: technology, system, service: Academic and educational guidebook. Kyiv, Ukraine: Start-98, 2007.
O. Terentiev, T. Prosiakina-Zharova, and V. Savastianov, “Application of tools for text analysys as an instrument for optimizing decision support in the tasks of eleborating plans of social and economic development of Ukraine”, Data recording, storage, and processing, vol. 18, no. 3, pp. 75-86, 2016.
B. Saracoglu, “An AHP Application in the Investment Selection Problem of Small Hydropower Plants in Turkey,” International Journal of the Analytic Hierarchy Process, vol. 7, iss. 2, pp. 211-239, 2015. doi:10.13033/ijahp.v7i2.198.
S. Mikoni, “System for choice and ranking SVIR’” in the Works of the international congress “Artificial intelligence in the XXI century”, Divnomorskoye, 2001, vol. 1, pp. 500-507.
D. Lande and Y. Kondratenko, “Features of construction systems of distributed content-monitoring of global information networks,” Information Technology and Security, vol. 5, iss. 1, pp. 5-11, January-June 2017. doi: 10.20535/2411-1031.2017.5.1.120550.
P. S. Addison, The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance. Boca Raton, USA: CRC Press, Taylor & Francis Group, 2017. doi: 10.1201/9781315372556.
D. Lande and A. Boichenko, “Scenario research based on analysis of information space,” Information Technology and Security, vol. 5, iss. 2, pp. 5-12, July-December 2017. doi: 10.20535/2411-1031.2017.5.2.136921.
This work is licensed under a Creative Commons Attribution 4.0 International License.
ISSN 2411-1031 (Print), ISSN 2518-1033 (Online)