تاثیر امنیت سایبری بر هوشمندسازی دولت الکترونیک با چارچوب امنیتی ارائه خدمات
محورهای موضوعی : مدیریت فناوری اطلاعاتبابک محمدحسینی 1 , سید فهیم قافله باشی 2 , مرتضی هادی زاده 3 *
1 - دانشگاه بین المللی امام خمینی(ره)
2 - شهید بهشتی
3 - دانشگاه شهید بهشتی
کلید واژه: دولت الکترونیک توسعه هوشمند هوشمند سازی امنیت سایبری پایگاه امنیت خدمات هوشمند,
چکیده مقاله :
با گسترش نوآوری در فناوری اطلاعات، جهان در حال تجربه تکامل هوشمند سازی و پیدایش شهرهای هوشمند است. دستگاههای هوشمند در بسیاری از شهرها مورد استفاده قرار میگیرند. خانه ها، اتومبیلها، اماکن عمومی و سایر سیستمهای اجتماعی اکنون در مسیر اتصال به "اینترنت اشیا" هستند. در روند هوشمند سازی، به شرط تامین امنیت و انتظارات ما از حریم خصوصی، ایجاد فرصتهای اقتصادی و اجتماعی جدید مورد انتظار خواهد بود. این پژوهش با در نظر گرفتن ضرورت گسترش هوشمند سازی و توجه به اهمیت امنیت در اين حوزه، به تبیین عوامل تاثیر گذار پرداخته است که منجر به ارائه مدل میشود. این مطالعه از منظر هدف، کاربردی و از جهت گردآوری اطلاعات، توصیفی- همبستگی است. گردآوری دادهها از طریق پرسشنامه محقق ساخته و با جامعهی نمونه ای شامل 120 نفر از متخصصین مدیریت فناوری اطلاعات و کارشناسان حوزه امنیت سایبری فعال در شهر تهران با روش نمونه گیری غیراحتمالی (در دسترس) و همچنین تجزیه و تحلیل دادهها از طریق نرمافزار پی ال اس انجام شده است. آلفاي کرونباخ براي تمامی سازه ها بالاتر از 7/. و مقدار میانگین واریانس استخراج شده بالاتر از 5/. بوده و روایی و پایایی سازهها تایید شد. در مدل ارائه شده مشخص میشود که توجه به چالش های مدرن امنیت سایبری به عنوان تاثیر مستقیم و همچنین، اصول تشکیل پایگاه امنیت و چارچوب ارائه خدمات هوشمند به عنوان عوامل میانجی در گسترش هوشمند سازی در دولت الکترونیک تاثیر معنا داری دارد.
With the spread of information technology innovation, the world is experiencing the evolution of smartening and the emergence of smart cities. Smart devices are used in many cities. Homes, cars, public places and other social systems are now connected to the Internet of Things. In the process of smartening, new economic and social opportunities are expected, provided we provide security and our expectations of privacy. This research, considering the need to expand intelligence and pay attention to the importance of security in this area, has explained the influential factors that lead to the presentation of the model. This study is applied in terms of purpose and descriptive-correlational in terms of data collection. Data collection was done through a researcher-made questionnaire and the sample population includes 120 IT management specialists and experts in the field of cyber security active in Tehran, which was done by non-probabilistic (available) sampling method and data analysis was also performed using PLS software. Cronbach's alpha for all structures is higher than 0.7 and the mean value of variance extracted is higher than 0.5, the validity and reliability were confirmed of the structures. The proposed model shows that paying attention to modern cyber security challenges as a direct impact, as well as the principles of establishing a security base and a framework for providing intelligent services as mediators have a significant impact on the development of smartening in e-government.
Ahmad, I., Abdullah, A. B., & Alghamdi, A. S. (2009). Application of artificial neural network in detection of DOS attacks. SIN’09 - Proceedings of the 2nd International Conference on Security of Information and Networks, 229–234. https://doi.org/10.1145/1626195.1626252
Ali, B., & Awad, A. I. (2018). Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes. In Sensors (Vol. 18, Issue 3). https://doi.org/10.3390/s18030817
Alqahtani, M., & Braun, R. (2021). Examining the Impact of Technical Controls, Accountability and Monitoring towards Cyber Security Compliance in E-government Organisations.
Alwan, Z. S., & Younis, M. F. (2017). Detection and Prevention of SQL Injection Attack: A Survey. International Journal of Computer Science and Mobile Computing, 6(8), 5–17. https://www.researchgate.net/publication/320108029
Anwar, A., & Hassan, S. I. (2017). Applying Artificial Intelligence Techniques to Prevent Cyber Assaults. International Journal of Computational Intelligence Research, 13(5), 883–889.
Bada, M., Sasse, A. M., & Nurse, J. R. C. (2019). Cyber Security Awareness Campaigns: Why do they fail to change behaviour? ArXiv. http://arxiv.org/abs/1901.02672
Banotra, A., Gupta, S., Gupta, S. K., & Rashid, M. (2021). Asset Security in Data of Internet of Things Using Blockchain Technology. In Springer (pp. 269–281). https://doi.org/10.1007/978-981-15-8711-5_14
Bayer, U., Moser, A., Kruegel, C., & Kirda, E. (2006). Dynamic analysis of malicious code. Journal in Computer Virology, 2(1), 67–77. https://doi.org/10.1007/s11416-006-0012-2
Bhatele, K. R., Shrivastava, H., & Kumari, N. (2019). The Role of Artificial Intelligence in Cyber Security. In igi-global.com (pp. 170–192). https://doi.org/10.4018/978-1-5225-8241-0.ch009
Boeckl, K., Fagan, M., Fisher, W., Lefkovitz, N., Megas, K. N., Nadeau, E., Gabel, D., Rourke, O. ’, Piccarreta, B., & Scarfone, K. (2019). NISTIR 8228 Considerations for Managing Internet of Things (IoT) Cybersecurity and Privacy Risks. 44. https://doi.org/10.6028/NIST.IR.8228
Chiew, K. L., Yong, K. S. C., & Tan, C. L. (2018). A survey of phishing attacks: Their types, vectors and technical approaches. In Expert Systems with Applications (Vol. 106, pp. 1–20). https://doi.org/10.1016/j.eswa.2018.03.050
Chowdhury, M., Rahman, A., & Islam, R. (2018). Malware analysis and detection using data mining and machine learning classification. Advances in Intelligent Systems and Computing, 580, 266–274. https://doi.org/10.1007/978-3-319-67071-3_33
Das, S., Kim, A., Tingle, Z., & Nippert-Eng, C. (2019). All about phishing exploring user research through a systematic literature review. In arXiv. arXiv. http://arxiv.org/abs/1908.05897
Elmaghraby, A. S., & Losavio, M. M. (2014). Cyber security challenges in smart cities: Safety, security and privacy. Journal of Advanced Research, 5(4), 491–497. https://doi.org/10.1016/j.jare.2014.02.006
Fischer, E. A. (2015). Cybersecurity issues and challenges: In Brief. In Cyberspace Threat Landscape: Overview, Response Authorities, and Capabilities (pp. 45–54). www.crs.gov
Foroughi, F., & Luksch, P. (2018). Data science methodology for cybersecurity projects. In arXiv. arXiv. https://doi.org/10.5121/csit.2018.80401
Furstenau, L. B., Sott, M. K., Homrich, A. J. O., Kipper, L. M., Al Abri, A. A., Cardoso, T. F., Lo´pez-Robles, J. R., & Cobo, M. J. (2020). 20 years of scientific evolution of cyber security: A science mapping. Proceedings of the International Conference on Industrial Engineering and Operations Management, March, 314–325. https://www.researchgate.net/profile/Leonardo_Bertolin_Furstenau/publication/340413661_20_Years_of_Scientific_Evolution_of_Cyber_Security_a_Science_Mapping/links/5e91e6234585150839d2b75e/20-Years-of-Scientific-Evolution-of-Cyber-Security-a-Science-Mapping
Geluvaraj, B., Satwik, P. M., & Ashok Kumar, T. A. (2019). The Future of Cybersecurity: Major Role of Artificial Intelligence, Machine Learning, and Deep Learning in Cyberspace. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 15, pp. 739–747). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-10-8681-6_67
Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques third edition. The Morgan Kaufmann Series in Data Management Systems, 5(4), 83–124.
Hasanova, H., Baek, U. jun, Shin, M. gon, Cho, K., & Kim, M. S. (2019). A survey on blockchain cybersecurity vulnerabilities and possible countermeasures. International Journal of Network Management, 29(2), e2060. https://doi.org/10.1002/nem.2060
Hou, Y., Garg, S., Hui, L., Jayakody, D. N. K., Jin, R., & Hossain, M. S. (2020). A data security enhanced access control mechanism in mobile edge computing. IEEE Access, 8, 136119–136130. https://doi.org/10.1109/ACCESS.2020.3011477
Huang, K., & Madnick, S. E. (2020). Does High Cybersecurity Capability Lead to Openness in Digital Trade? The Mediation Effect of E-Government Maturity within Cross-border Digital Innovation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3542552
Humayun, M., Niazi, M., Jhanjhi, N., Alshayeb, M., & Mahmood, S. (2020). Cyber Security Threats and Vulnerabilities: A Systematic Mapping Study. Arabian Journal for Science and Engineering, 45(4), 3171–3189. https://doi.org/10.1007/s13369-019-04319-2
Kissoon, T. (2020). Optimum spending on cybersecurity measures. Transforming Government: People, Process and Policy, 14(3), 417–431. https://doi.org/10.1108/TG-11-2019-0112
Kumaresan, S., & Shanmugam, V. (2020). Time-variant attribute-based multitype encryption algorithm for improved cloud data security using user profile. Journal of Supercomputing, 76(8), 6094–6112. https://doi.org/10.1007/s11227-019-03118-8
Lastdrager, E. E. H. (2014). Achieving a consensual definition of phishing based on a systematic review of the literature. In Crime Science (Vol. 3, Issue 1). SpringerOpen. https://doi.org/10.1186/s40163-014-0009-y
Leenen, L., & Meyer, T. (2019). Artificial Intelligence and Big Data Analytics in Support of Cyber Defense. In igi-global.com (pp. 42–63). https://doi.org/10.4018/978-1-5225-8304-2.ch002
Li, J.-H. (2018). Cyber security meets artificial intelligence: a survey *. Inform Technol Electron Eng, 19(12), 1462–1474. https://doi.org/10.1631/FITEE.1800573
Lun, Y. Z., D’Innocenzo, A., Malavolta, I., & Di Benedetto, M. D. (2016). Cyber-Physical Systems Security: a Systematic Mapping Study. Journal of Systems and Software, 149, 174–216. https://doi.org/10.1016/j.jss.2018.12.006
Mahjabin, T., Xiao, Y., Sun, G., & Jiang, W. (2017). A survey of distributed denial-of-service attack, prevention, and mitigation techniques. International Journal of Distributed Sensor Networks, 13(12), 2017. https://doi.org/10.1177/1550147717741463
Mallik, A., Ahsan, A., Shahadat, M. M. Z., & Tsou, J. C. (2019). Man-in-the-middle-attack: Understanding in simple words. International Journal of Data and Network Science, 3(2), 77–92. https://doi.org/10.5267/j.ijdns.2019.1.001
Mallikarjunan, K. N., Muthupriya, K., & Shalinie, S. M. (2016). A survey of distributed denial of service attack. Proceedings of the 10th International Conference on Intelligent Systems and Control, ISCO 2016, 1–6. https://doi.org/10.1109/ISCO.2016.7727096
Martínez Torres, J., Iglesias Comesaña, C., & García-Nieto, P. J. (2019). Review: machine learning techniques applied to cybersecurity. International Journal of Machine Learning and Cybernetics, 10(10), 2823–2836. https://doi.org/10.1007/s13042-018-00906-1
Mora, O. B., Rivera, R., Larios, V. M., Beltran-Ramirez, J. R., Maciel, R., & Ochoa, A. (2019, February 28). A Use Case in Cybersecurity based in Blockchain to deal with the security and privacy of citizens and Smart Cities Cyberinfrastructures. 2018 IEEE International Smart Cities Conference, ISC2 2018. https://doi.org/10.1109/ISC2.2018.8656694
Natarajan, J. (2019). Cyber secure man-in-the- middle attack intrusion detection using machine learning algorithms. In AI and Big Data’s Potential for Disruptive Innovation (pp. 291–316). https://doi.org/10.4018/978-1-5225-9687-5.ch011
Panimalar, A., Pai, G., & Khan, S. (2018). Artificial intelligence techniques for cyber security. International Research Journal of Engineering and Technology, 5(3), 122–124.
Papastergiou, S., Mouratidis, H., & Kalogeraki, E. M. (2019). Cyber security incident handling, warning and response system for the european critical information infrastructures (cyberSANE). Communications in Computer and Information Science, 1000, 476–487. https://doi.org/10.1007/978-3-030-20257-6_41
Parizi, R. M., Dehghantanha, A., Azmoodeh, A., & Choo, K. K. R. (2020). Blockchain in cybersecurity realm: An overview. In Advances in Information Security (Vol. 79, pp. 1–5). Springer. https://doi.org/10.1007/978-3-030-38181-3_1
Pingle, B., Mairaj, A., & Javaid, A. Y. (2018). Real-World Man-in-the-Middle (MITM) Attack Implementation Using Open Source Tools for Instructional Use. IEEE International Conference on Electro Information Technology, 2018-May, 192–197. https://doi.org/10.1109/EIT.2018.8500082
Purkait, S. (2012). Phishing counter measures and their effectiveness - Literature review. In Information Management and Computer Security (Vol. 20, Issue 5, pp. 382–420). Emerald Group Publishing Limited. https://doi.org/10.1108/09685221211286548
Qinxia, H., Nazir, S., Li, M., Ullah Khan, H., Lianlian, W., & Ahmad, S. (2021). AI-Enabled Sensing and Decision-Making for IoT Systems. In Complexity (Vol. 2021). https://doi.org/10.1155/2021/6616279
Sabar, N. R., Yi, X., & Song, A. (2018). A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security. IEEE Access, 6, 10421–10431. https://doi.org/10.1109/ACCESS.2018.2801792
Sani, A. S., Yuan, D., Jin, J., Gao, L., Yu, S., & Dong, Z. Y. (2019). Cyber security framework for Internet of Things-based Energy Internet. Future Generation Computer Systems, 93, 849–859. https://doi.org/10.1016/j.future.2018.01.029
Sarker, I. H., Colman, A., & Han, J. (2019). RecencyMiner: mining recency-based personalized behavior from contextual smartphone data. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0211-6
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data, 7(1), 1–29. https://doi.org/10.1186/s40537-020-00318-5
Scarfò, A. (2017). The Cyber Security Challenges in the IoT Era. In Security and Resilience in Intelligent Data-Centric Systems and Communication Networks (pp. 53–76). Elsevier. https://doi.org/10.1016/B978-0-12-811373-8.00003-3
Schiavone, F., Appio, F. P., Mora, L., & Risitano, M. (2020). The strategic, organizational, and entrepreneurial evolution of smart cities. International Entrepreneurship and Management Journal, 16(4), 1155–1165. https://doi.org/10.1007/s11365-020-00696-5
Serrano, O., Dandurand, L., & Brown, S. (2014). On the design of a cyber security data sharing system. Proceedings of the ACM Conference on Computer and Communications Security, 2014-Novem(November), 61–69. https://doi.org/10.1145/2663876.2663882
Tang, Y., & Chen, S. (2007). An automated signature-based approach against polymorphic internet worms. IEEE Transactions on Parallel and Distributed Systems, 18(7), 879–892. https://doi.org/10.1109/TPDS.2007.1050
Thomas, J. E. (2018). Individual Cyber Security: Empowering Employees to Resist Spear Phishing to Prevent Identity Theft and Ransomware Attacks. International Journal of Business and Management, 13(6), 1. https://doi.org/10.5539/ijbm.v13n6p1
Thomas, T., P. Vijayaraghavan, A., Emmanuel, S., Thomas, T., P. Vijayaraghavan, A., & Emmanuel, S. (2020). Machine Learning and Cybersecurity. In Machine Learning Approaches in Cyber Security Analytics (pp. 37–47). Springer Singapore. https://doi.org/10.1007/978-981-15-1706-8_3
Usman, M., Jan, M. A., He, X., & Chen, J. (2019). A survey on representation learning efforts in cybersecurity domain. In ACM Computing Surveys (Vol. 52, Issue 6, pp. 1–28). Association for Computing Machinery. https://doi.org/10.1145/3331174
Vyamajala, S., Mohd, T. K., & Javaid, A. (2018). A Real-World Implementation of SQL Injection Attack Using Open Source Tools for Enhanced Cybersecurity Learning. IEEE International Conference on Electro Information Technology, 2018-May, 198–202. https://doi.org/10.1109/EIT.2018.8500136
Wang, L., An, H., & Chang, Z. (2020). Security Enhancement on a Lightweight Authentication Scheme with Anonymity Fog Computing Architecture. IEEE Access, 8, 97267–97278. https://doi.org/10.1109/ACCESS.2020.2996264
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. IEEE Access, 6, 35365–35381. https://doi.org/10.1109/ACCESS.2018.2836950
Yao, M., Sohul, M., Marojevic, V., & Reed, J. H. (2019). Artificial Intelligence Defined 5G Radio Access Networks. IEEE Communications Magazine, 57(3), 14–20. https://doi.org/10.1109/MCOM.2019.1800629
Ye, Y., Li, T., Adjeroh, D., & Iyengar, S. S. (2017). A survey on malware detection using data mining techniques. ACM Computing Surveys, 50(3). https://doi.org/10.1145/3073559
You, X., Zhang, C., Tan, X., Jin, S., & Wu, H. (2019). AI for 5G: research directions and paradigms. In Science China Information Sciences (Vol. 62, Issue 2). Science in China Press. https://doi.org/10.1007/s11432-018-9596-5
Yu, W., Zhang, N., Fu, X., & Zhao, W. (2010). Self-disciplinary worms and countermeasures: Modeling and analysis. IEEE Transactions on Parallel and Distributed Systems, 21(10), 1501–1514. https://doi.org/10.1109/TPDS.2009.161
Zeadally, S., Adi, E., Baig, Z., & Khan, I. A. (2020). Harnessing artificial intelligence capabilities to improve cybersecurity. IEEE Access, 8, 23817–23837. https://doi.org/10.1109/ACCESS.2020.2968045
Zhao, S., Leftwich, K., Owens, M., Magrone, F., Schonemann, J., Anderson, B., & Medhi, D. (2014). I-CaN-MaMa: Integrated campus network monitoring and management. IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World. https://doi.org/10.1109/NOMS.2014.6838304
Zhuang, P., Zamir, T., & Liang, H. (2021). Blockchain for Cybersecurity in Smart Grid: A Comprehensive Survey. IEEE Transactions on Industrial Informatics, 17(1), 3–19. https://doi.org/10.1109/TII.2020.2998479
bm security report, https://www.ibm.com/security/data-breach. Accessed on 20 Oct 2019.
Garnett, H. A., & James, T. S. (2020). Cyber Elections in the Digital Age: Threats and opportunities of technology for electoral integrity. Election Law Journal: Rules, Politics, and Policy, 19(2), 111-126.
Gunduz, M. Z., & Das, R. (2020). Cyber-security on smart grid: Threats and potential solutions. Computer networks, 169, 107094.
Europol: Attacks On Critical Infrastructures (2020). https://www.europol.europa.eu/iocta/2015/attacks-on-ci.html. Accessed 10 July 2020
Libor, D. (2017). Concerning measures for a high common level of security of network and information systems across EU (Doctoral dissertation, Тернопіль, ТНЕУ).
سرمد، بازرگان و حجازي، الهه، 1387، روشهاي تحقیق در علوم رفتاري. تهران: آگاه.