پیشران‌های ارائه خدمات سایبری پایدار در دولت با تاکید بر حفظ امنیت از طریق هوش مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دانشگاه بین المللی امام خمینی(ره،) قزوین، ایران

2 دانش‌آموخته کارشناسی‌ارشد مدیریت‌کارآفرینی سازمانی،دانشگاه شهید بهشتی تهران،ایران

3 دانشجوی کارشناسی ارشد مدیریت فناوری اطلاعات، دانشگاه شهید بهشتی تهران، ایران

چکیده

هدف: با توجه به توسعه‌ی روزافزون فناوری اطلاعات، این خطر پیش‌بینی می‌شود که در آینده‌ی نزدیک ساختارهای سازمانی از ترس عقب‌ماندگی شتابزده عمل کرده و بدون توجه کافی به ابعاد امنیتی، صرفاً برای سایبری‌سازی و تخصیص هزینه‌های کلان جهت آماده‌سازی زیرساخت‌های فنی، از توجه به ضرورت برقراری امنیت هوشمند غفلت کنند. در این خصوص این پژوهش سعی دارد تا با رعایت ابعاد امنیتی، به شناسایی و اولویت‌بندی پیش‌ران‌هایی بپردازد که بیشترین قابلیت ارائه‌ی خدمت در حوزه‌ی سایبری را داشته باشند.
روش: روش پژوهش حاضر توصیفی– تحلیلی است و همچنین، روش گردآوری اطلاعات در بخش نظری، مطالعات کتابخانه‌ای و ابزار گردآوری اطلاعات در بخش تحلیلی، پرسشنامه و تحلیل داده‌ها با نرم افزارهای «اس. پی. اس. اس» و«میک مک» انجام شده است.
یافته‌ها: با درنظر گرفتن بُعد امنیت بر اساس نظر خبرگان، 12 پیش‌ران که دارای بیشترین پتانسیل ارائه‌ی خدمت در حوزه‌ی سایبری هستند، شناسایی و در 4 محور اولویت‌بندی شدند. در ادامه با در نظر گرفتن دو شاخص اثرگذاری و اثرپذیری، به کشف روابط بین پیش‌ران‌ها پرداخته و در نهایت سعی شد تا با تجویز رویه‌ی مناسب، سیستم به پایداری نزدیک شود.
نتیجه‌گیری: با توجه به نتایج تحقیق لازم است که دولت در راستای ارائه‌ی خدمات سایبری، میزان تأثیرپذیری و تأثیرگذاری سازمان از یکدیگر را در نظر گرفته و از اخذ تصمیمات پراکنده که اولویت‌بندی مشخصی ندارد، اجتناب کند، و همچنین در تحقق خدمات سایبری بر رعایت بعد امنیت اهتمام ویژه‌ای داشته باشد.

کلیدواژه‌ها

عنوان مقاله [English]

The Drivers of Sustainable Cyber Service Offer in the Government with an Emphasis on Maintaining Security Using Artificial Intelligence

نویسندگان [English]

  • Babak Mohammadhosseini 1
  • Morteza Hadizadeh 2
  • Sayyed Fahim Ghafelebashi 3
1 Assistant Professor, Imam Khomeini International University, Qazvin, Iran.
2 M.A. in Organizational Entrepreneurship Management, Shahid Beheshti Universit,Tehran, Iran
3 M.A. Student in Information Technology Management, Shahid Beheshti Universit,Tehran, Iran
چکیده [English]

Purpose: Due to the increasing development of information technology, researchers estimate that in the near future, organizational structures will act hastily for fear of backwardness. Without sufficient attention to the security dimensions, they ignore the need for intelligent security simply by emphasizing cyberization and allocating large costs for preparing the technical infrastructure. In this regard, by observing the security dimensions, our research tries to identify and prioritize the drivers that have the most ability to provide cyber services.
Method: Our research is descriptive-analytical. The data collection is done theoretically in accordance with library studies; its tool is analytically questionnaire and data analysis is conducted by SPSS and Mic-Mac software.
Findings: Considering the security dimensions according to experts, we identified 12 drivers with the highest potential to provide cyber services and prioritized them in 4 areas. Next, by considering the two parameters of action and reaction, we explored the relationships between the drivers. Finally, we tried to bring the system closer to stability by prescribing an appropriate procedure.
Conclusion: According to the results of the research, in order to provide cyber services, the government should consider the degree of the organization's action and reaction and avoid making sporadic decisions that do not have a specific priority. In the realization of its cyber services, it should also pay special attention to the security dimension.

کلیدواژه‌ها [English]

  • Futures Studies
  • Cyber Security
  • Artificial Intelligence
  • e-Government
  • Mic Mac

مراجع

 
1. شجاعان، امیر و همکاران. (1398). تحقق حاکمیت الکترونیک ایران: گامی به سوی دولت هوشمند. دو فصلنامه‌ی علمی پژوهشی مدیریت بحران.8(1): 49-59.
2. طبائیان، کمال. (1388). دلفی یکی از فنون مورد استفاده در آینده‌پژوهی. فصلنامه‌ی آینده‌پژوهی، مفاهیم و روش‌ها، شماره‌ی 4: 127-140.
3. علی اکبری، اسماعیل و همکاران. (1397). شناسایی پیش‌ران‌های مؤثر بر وضعیت آینده‌ی گردشگری پایدار شهر کرمان با رویکرد آینده‌پژوهی. فصلنامه‌ی علمی‌پژوهشی گردشگری و توسعه‌ی. 7(1): 156-178.
 
1.   Abbas, N., Tanveer A., Ullah Shah, S. H., Omar, M., & Woo Park, H. (2019). Investigating the applications of artificial intelligence in cyber security. Scientometricsvolume, 121(2):1189–1211.
2.   Aftergood, S. (2017). Cybersecurity: The cold war online,'' Nature, 547(2):30-31.
3.   Akhtar, N. & Mian, A. (2018). Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access, 6:14410 - 14430.
4.   Al Lily, A., Fathy Ismail, A., Abunasser, F., & Alqahtani, R. (2020). Distance Education as a Response to Pandemics: Coronavirus and Arab Culture. Technology in Society,63(2): 105-120.
5.   AliAkbar, I., Ahmadpour, A., & Jalalabadi, L. (1397). Identifying drivers for the future of sustainable tourism in Kerman with a futures research approach. Journal of Tourism and Development. 7: 156-175. (In Persian)
6.   Anon, N. (2020). [online] Pros and Cons of Artificial Intelligence: https://www.linkedin.com/pulse/pros-cons-artificial-intelligence-mikefekety.
7.   Barth, T., Arnold, E. .(1999). Artificial intelligence and administrative discretion: implications forpublic administration.Am. Rev. Public Adm,29(4): 332–351.
8.   Bhatele, K., Sharivastava, H., & Kumari, N. (2017) The Role of ArtificialIntelligence in Cyber Security. Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems, 2017: 175-176.
9.   Bournaris, T. (2020). Evaluation of e-GovernmentWeb Portals: The Case of Agricultural e-Government Services in Greece. Agronomy, 10(7):932.
10.  Bredillet, C., Tywoniak, S., & Tootoonchy, M. (2018). Why and how do project management offices change? A structural analysis approach. International Journal of Project Management, 36(5), 744–761.
11.  Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2018). Artificial intelligence–the next digital frontier. McKinsey Glob Institute, 17: 224-226.
12.  Dasoriya R., Rajpopat J., Jamar R., & Maurya, M. (2018). The Uncertain Future of Artificial Intelligence,2018 8th International Conference on Cloud Computing, Data Science & Engineering,: 458-461.
13.  Deibert, R. & Rohozinski, R. (2010).risking security: Policies and paradoxes of cyberspace security. International Political Sociology, 4 (1): 15– 32.
14.  Duperrin, J., & Godet, M. (1973). Methode de hierarchisation des elements d'un Systeme, Rapport Economique du CEA, 5: 45-51.
15.  Dubey, R. & Ali, S. (2014). Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis. Global Journal of Flexible Systems Management, 15 (2), 131–143.
16.  Gasova, K.  & Stofkova, K. (2017). E-Government as a quality improvement tool for citizens' services. TRANSCOM 2017: International scientific conference on sustainable, modern and safe transport,192(1): 225 – 230.
17.  Geluvaraj, B., Satwik, P., & Ashok Kumar, T. (2019). The Future of Cybersecurity: Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Cyberspace. Conference: International Conference on SecurityAt: Garden City University, Bangalore.
18.  Godet, A. J., Meunier, M. F., & Roubelat, F.,(2003). Structural analysis with the MICMAC,method & actors’ strategy with MACTOR method, FuturesResearch Methodology, 2:135-140.
19.  Godet, M. (1991). From anticipation to action, UNESCO publishing. Paris.
20.  Golovko, V. (2017). Deep learning: an overview and main paradigms. Opt Memory Neur Netw, 26(1):1-17.
21.  Guan, ZT., Li J., & Wu L. F. (2017). Achieving efficient and secure data acquisition for cloud-supported Internet of Things in smart grid.IEEE Internet Things, 4(6): 1934-1944.
22.  Hamet, P. & Tremblay, J. (2017) Artificial intelligence in medicine. Metabolism journal, 2017: 89-12.
23.  Hung, S., Chang, C., & Yu, T. (2006). Determinants of user acceptance of the e-government services:the case of online tax filing and payment system. Gov. Inf. Q, 23(1): 97–122.
24.  Iqbal, S. & Pippon-Young, L. (2009). The Delphi method. Nursing Research, 46(2), 116–118.
25.  Janevski, Z et al. (2014). Business benefits from e-government services: case of Slovenia and Macedonia. Economic Development, 12(3): 13-27.
26.  Jian-Hua, L. (2018). Cyber security meets artificial intelligence: a survey. Frontiers of Information Technology & Electronic Engineering, 19: 1462-1474.
27.  Johnson, J. (2019). Artificial intelligence & future warfare:implications for international security Defense & Security Analysis,35: 1-23.
28.  Kalbaska, N., Janowski, T., Estevez, E,. & Cantoni, L. (2016). E-Government Relationships Framework in the Tourism Domain. A First Map.  Information and Communication Technologies in Tourism - Proceedings of the International Conference in Bilbao (Spain). New York, pp: 73-87.
29.  Kahn, H. & Wiener, A. (1967). The Next Thirty-Three Years: A Framework for Speculation. Dædalus: 705-732.
30.  Kitsing, M. (2017). Internet Banking as a Platform for E-Government. Annual International Conference on Innovation and Entrepreneurship,30: 99-107
31.  Kumar, N., Kharkwal, N., Kohli, R., & Choudhary, S. (2016). Ethical aspects and future of artificial intelligence.2016 International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH: 111-114
32.  Lamberti, L., Benedetti, M., & Chen, S.(2014). Benefits sought by citizens and channel attitudes formultichannel payment services: evidence from Italy. Gov. Inf. Q. 31(4): 596-609.
33.  Larrocha, E., Minguet, J., Díaz, G., Castro, M., & Vara, A. (2010). Filling the gap of Information Security Management inside ITIL: proposals for postgraduate students. IEEE EDUCON Edu. Engg, pp: 907-912.
34.  Lengler, R. & Eppler, M. (2007). Towards a periodic table of visualization methods for management. InProceedings of the IASTED International Conference on Graphics and Visualization in Engineering, pp: 83–88.
35.  Li, L., Ota, K., & Dong, M. X. (2018). Deep learning for smart industry. efficient manufacture inspection system with fog computing. IEEE Trans Ind Inform, 14(10): 4665- 4673.
36.  Liu, S., Cui, W., Wu, Y., & Liu, M. (2014). A survey on information visualization: Recent advances and challenges. The Visual Computer, 30(12): 1373–1393
37.  Lv, Z,. Li, X,. Wang, W,. Zhang, B,. Hu, J,. & Feng, S. (2017).Government affairs service platform for smart city. Future Generation Computer Systems: 81.
38.  Magro, M. (2012). A Review of Social Media Use in E-Government. Administrative Sciences, 2(2):148-161.
39.  Meyer, J. A. (1997). The acceptance of visual information in management. Information & Management, 32(6): 275–287.
40.  Mikhaylov, S., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector:opportunities and challenges of cross-sector collaboration. Philos. Trans. R. Soc, 37(5): 124-140.
41.  Milenkoski A., Vieira M., Kounev S., Avritzer A., & Payne, B. D. (2015). Evaluating computer intrusion detection systems: A survey of commonpractices, ACM Comput. Surv.,48(2):1-41.
42.  Mirza, E. & Ehsan, N. (2017). Quantification of project execution complexity and its effect on performance of infrastructure development projects. Engineering Management Journal, 29(2), 108–123.
43.  Mohd Saman, W. & Haider, A. (2012). Electronic court records management in Malaysia: A case study. Journal of e-Government Studies and Best Practices. 2: 1122-1133.
44.  Nappo, S. (2017). The Role of Artificial Intelligencein Cyber Security. Goodreads.75(2): 112-138.
45.  Pereira, T. & Santos, H. (2010). A security audit framework to manage Information. Global Security, Safety and Sustainability: 9-18.
46.  Reis, J., Amorim, M., Melão, N., & Matos, P. (2018). Digital transformation: a literature review andguidelines for future research. In: Trends and Advances in Information Systems andTechnologies, WorldCIST, Springer, 2018: 411-421.
47.  Saatcioglu, O., Deveci, D., & Cerit, G. (2009). Logistics and transportation information systems in Turkey: E-government perspectives. Transforming Government People Process and Policy. 3(2):144-162.
48.  Shanmugam K., Khairunnisha Zainal, N., & Gnanasekaren, Ch.(2019). Technology Foresight In The Virtual Learning Environment in Malaysia. Journal of Physics: Conference Series. 1228.
49.  Shojaan, A., Taghavifard, T,. Elyasi, M., & Mohammadi, M. (2018). The Realization of Electronic Governance in Iran: A Step to the Intelligent Government. Journal of Emergency Management (JOEM).8: 49-59. (In Persian).
50.  Sundberg, L. (2019). Electronic government: Towards e-democracy or democracy at risk?.Safety Science,118:22:23.
51.  Tabaeyan, K. (1388). Delphi is one of the techniques used in Futurology, Concepts and Methods), Defense Educational and Research Institute, 1388:127-1.(In Persian)
52.  Thuraisingham, B. M. (2020). Can AI be for Good in the Midst of Security Attacks and Privacy Violations?.Proceedings ACM CODASPY, 2020: 1-10.
53.  Wazid M., Zeadally S., & Das A. K. (2019). Mobile banking: Malware threats and security solutions, IEEE ConsumerElectronics Magazine, 8: 56-60.
54.  Wegmann, A., Regev, G., Garret, G., & Maréchal, F. (2008). Specifying Services for ITIL Service Management. Proc. Int. Workshop Service-Oriented Computing Consequences for Engineering Requirements, 2008:1-8.
55.  Wirtz, B., Weyerer, J., & Geyer, C. (2018). Artificial intelligence and the public sector–applicationsand challenges. Int. J. Public Adm, 13(7): 1–20
56.  Wong, M. & Jackson, S. (2017). User Satisfaction Evaluation of Malaysian E-Government Education Services. 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC): 27-29.
57.  Woudenberg, f.(1991). an evaluation of  Delphi . Technological forecasting and social change, 40: 131-150.
58.  Zegers, N. (2006). A methodology for improving information security incident identification and response. Master Thesis Inform.& Econom, Erasmus Univ. Rotterdam, 18: 57-60.

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