Economic analysis of data protection in systems with complex architecture using neural network methods
Economic Annals-ХХI: Volume 185, Issue 9-10, Pages: 178-188
Citation information:
Nelub, V., Gantimurov, A., & Borodulin, A. (2020). Economic analysis of data protection in systems with complex architecture using neural network methods. Economic Annals-XXI, 185(9-10), 178-188. doi: https://doi.org/10.21003/ea.V185-17
Vladimir Nelub
PhD (Engineering),
Bauman Moscow State Technical University;
Director,
Interdisciplinary Engineering Centre «Composites of Russia» of the Bauman Moscow State Technical University
1 Lefortovskaya Embankment, Moscow, 105005, Russian Federation
mail@emtc.ru
ORCID ID: https://orcid.org/0000-0001-6712-7135
Andrey Gantimurov
PhD Student (Economics),
Bauman Moscow State Technical University;
Chief Technology Officer,
BAUM-Inform
1 Lefortovskaya Embankment, Moscow, 105005, Russian Federation
a.gantimurov@npobaum.ru
ORCID ID: https://orcid.org/0000-0002-5000-702X
Alexey Borodulin
PhD (Engineering),
Bauman Moscow State Technical University;
Deputy Director,
Interdisciplinary Engineering Centre «Composites of Russia» of the Bauman Moscow State Technical University
1 Lefortovskaya Embankment, Moscow, 105005, Russian Federation
asb@emtc.ru
ORCID ID: https://orcid.org/0000-0003-4448-6762
Economic analysis of data protection in systems with complex architecture using neural network methods
Abstract. Introduction. In the United States, Europe, and Asia, there have been spikes in cyber attacks on protected and confidential information (including bank data, personal data, and confidential business information) over the period 2000-2020.
Data protection in systems with complex architectures is a complex and non-trivial solution, which is suitable for flexible self-tuning and self-learning tools, such as neural networks as state modern studies.
The described above state of things stipulates the importance and topicality of the direction of our research.
Our research raises the question about the economic study of the data but attempts to create a mathematical apparatus to cause a loss of data in digital form, not made before.
The purpose of the study is to propose and test the calculation of the economic value of the data loss and economic benefit from data protection with multi-level neural networks.
Results. Nowadays, almost every business that has access to the Internet has a threat of losing protected data, including banking data. To prevent this, the company must comply with the data protection management regulations. As an example, we studied the process of neural network reaction to an attack and its detection, when two types of attacks are threatened: botnet and UDP flood. Due to the result of a quick response to detect suspicious activity, neural network methods are excellent for use in economic analysis, because the neural network logs every event for every millisecond.
Conclusions. As a result of using neural network tools for economic analysis of data protection in modern systems with complex architecture, we were able to obtain stable results of responding to an emerging attack in order to obtain protected data. Such protection is a preventive measure for the possibility of reducing business losses in the event of data loss. As we can see, the management of protection of systems with complex architecture is necessary for each company with the specified data level.
Keywords: Information Security; Data Protection; Neural Network; Economic Analysis; Cyber Attack
JEL Classіfіcatіon: C15; L78
Acknowledgements and Funding: The authors received no direct funding for this research.
Contribution: The authors contributed equally to this work.
DOI: https://doi.org/10.21003/ea.V185-17
References
- Abelson, R., & Goldstein, M. (2015, February 5). Millions of Anthem Customers Targeted in Cyberattack. The New York Times.
http://www.nytimes.com/2015/02/05/business/hackers-breached-data-of-millions-insurer-says.html - Ablon, L., Heaton, P., Lavery, D., & Romanosky, S. (2016) Consumer Attitudes Toward Data Breach Notifications and Loss of Personal Information. RAND Corporation.
https://www.rand.org/pubs/research_reports/RR1187.html - Algarni, A. M., & Malaiya, Y. K. (2016). A consolidated approach for estimation of data security breach costs. In 2016 2nd International Conference on Information Management (ICIM) (pp. 26-39). IEEE.
https://www.cs.colostate.edu/~malaiya/p/breachcostAlgarni2016.pdf - Anderson, E. T., Fong, N. M., Simester, D. I., & Tucker, C. E. (2010). How Sales Taxes Affect Customer and Firm Behavior: The Role of Search on the Internet. Journal of Marketing Research, 47(2), 229-39.
https://doi.org/10.1509%2Fjmkr.47.2.229 - Arief, B., Adzmi, M. A. B., & Gross, T. (2015). Understanding cybercrime from its stakeholders’ perspectives: Part 1-attackers. IEEE Security & Privacy, 13(1), 71-76.
https://doi.org/10.1109/MSP.2015.19 - Avigur-Eshel, Amit. (2018). Synthesizing Depoliticization and Responsibilization: The Case of Financial Education in Israel. Competition & Change, 22(5), 509-528.
https://doi.org/10.1177/1024529418798115 - Bawden, D., & Robinson, L. (2008). The Dark Side of Information: Overload, Anxiety and Other Paradoxes and Pathologies. Journal of Information Science, 35(2), 180-191.
https://doi.org/10.1177/0165551508095781 - Biener, C., Eling, M., & Wirfs, J. H. (2015). Insurability of cyber risk: an empirical analysis. The Geneva Papers on Risk and Insurance – Issues and Practice, 40, 131-158.
https://doi.org/10.1057/gpp.2014.19 - Campbell, H. (2012). Planning to Change the World: Between Knowledge and Action Lies Synthesis. Journal of Planning Education and Research, 32(2), 135-146.
https://doi.org/10.1177/0739456X11436347 - Carr, M. (2016). Public-Private Partnerships in National Cyber-Security Strategies. International Affairs, 92(1), 43-62.
https://doi.org/10.1111/1468-2346.12504 - Chang, L. Y. C., Zhong, L. Y., & Grabosky, P. N. (2018). Citizen Co-production of Cyber Security: Self-Help, Vigilantes, and Cybercrime. Regulation & Governance, 12(1), 101-114.
https://doi.org/10.1111/rego.12125 - Cybersource. (2019). 2019 Global eCommerce Fraud Management Report.
https://www.cybersource.com/content/dam/documents/en/global-fraud-report-2019.pdf - de Bruijn, H., & Janssen, M. (2017). Building Cybersecurity Awareness: The Need for Evidenced-Based Framing Strategies. Government Information Quarterly, 34(1), 1-7.
https://doi.org/10.1016/j.giq.2017.02.007 - Edwards, B., Hofmeyr, S., & Forrest, S. (2015). Hype and heavy tails: a closer look at data breaches. Workshop on the Economics of Information Security.
https://econinfosec.org/archive/weis2015/papers/WEIS_2015_edwards.pdf - Fitzgerald, C., & Cunningham, James, A. (2016). Inside the University Technology Transfer Office: Mission Statement Analysis. Journal of Technology Transfer, 41(5), 1235-1246.
https://doi.org/10.1007/s10961-015-9419-6 - Gordon, L. A., Loeb, M. P., Lucyshyn, W., & Zhou, L. (2015). Increasing cybersecurity investments in private sector firms. Journal of Cybersecurity, 1(1), 3-17.
https://doi.org/10.1093/cybsec/tyv011 - Hadjimatheou, K. (2019, October 16). Citizen-Led Digital Policing and Democratic Norms: The Case of Self-Styled Paedophile Hunters. Criminology & Criminal Justice.
https://doi.org/10.1177/1748895819880956 - Kafali, O., Jones, J., Petruso, M., Williams, L., & Singh, M. P. (2017). How good is a security policy against real breaches? A HIPAA case study. In 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE) (pp. 530-540). IEEE.
https://doi.org/10.1109/ICSE.2017.55 - McLeod, A., & Dolezel, D. (2018). Cyber-analytics: Modeling factors associated with healthcare data breaches. Decision Support Systems, 108, 57-68.
https://doi.org/10.1016/j.dss.2018.02.007 - Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016). A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing, 67, 1-67.
https://asp-eurasipjournals.springeropen.com/articles/10.1186/s13634-016-0355-x - Romanosky, S., Ablon, l., Kuehn, A., & Jones, T. (2017). Content analysis of cyber insurance policies: How do carriers write policies and price cyber risk? Working Paper, RAND Justice, Infrastructure, and Environment.
https://weis2017.econinfosec.org/wp-content/uploads/sites/3/2017/06/WEIS_2017_paper_28.pdf - Romanosky, S., Hoffman, D., & Acquisti, A. (2014). A empirical analysis of data breach litigation. Journal of Empirical Legal Studies, 11(1), 74-104.
https://doi.org/10.1111/jels.12035 - Ruffle, S. J., Bowman, G., Caccioli, F., Coburn, A., Kelly, S., Leslie, B., & Ralph, D. (2014). Stress test scenario: Sybil logic bomb cyber catastrophe. Cambridge: Cambridge Risk Framew. Ser. Cent. Risk Stud. Univ.
https://www.researchgate.net/publication/263262710_
Stress_Test_Scenario_Sybil_Logic_Bomb_Cyber_Catastrophe - Sen, R., & Borle, S. (2015). Estimating the contextual risk of data breach: An empirical approach. Journal of Management Information Systems, 32(2), 314-341.
https://doi.org/10.1080/07421222.2015.1063315 - Shrouf, F., & Miragliotta, G. (2015). Energy Management Based on Internet of Things: Practices and Framework for Adoption in Production Management. Journal of Cleaner Production, 100, 235-246.
https://doi.org/10.1016/j.jclepro.2015.03.055 - Shu, X., Tian, K., Ciambrone, A., & Yao, D. (2017). Breaking the Target: An Analysis of Target Data Breach and Lessons Learned. CoRR, abs/1701.04940.
https://arxiv.org/abs/1701.04940 - Souri, A., & Hosseini, R. (2018). A state-of-the-art survey of malware detection approaches using data mining techniques. Hum Centric Comput Inform Sciences, 8(3), 1-3.
https://hcis-journal.springeropen.com/articles/10.1186/s13673-018-0125-x - Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12, 493-501.
https://doi.org/10.1007/s12083-017-0630-0 - Ucci, D., Aniello, L., & Baldoni, R. (2017) Survey on the usage of machine learning techniques for malware analysis. Computers & Security, 81.
https://doi.org/10.1016/j.cose.2018.11.001 - Zhu, J., Huang, H., & Zhang, D. (2017). «Big Tigers, Big Data»: Learning Social Reactions to China’s Anticorruption Campaign through Online Feedback. Public Administration Review, 79(4), 500-513.
https://doi.org/10.1111/puar.12866
Received 1.08.2020
Received in revised form 29.09.2020
Accepted 16.10.2020
Available online 21.11.2020