The Purpose of Cybersecurity Governance in the Digital Transformation of Public Services and Protecting the Digital Environment
Article Sidebar
Main Article Content
Abstract
As it tries to incorporate computer-based technology into the public services provided by businesses or organisations, the digital transformation process is currently regarded one of the most prominent topics in circulation. Establishing fundamentals and points is necessary for digital transformation, as is relying on a specific set of employee talents and actively incorporating customers in the process's evolution. Cybersecurity has become an increasingly important issue for governments and businesses worldwide, and this shift towards digital services is a top priority for all of them. Accelerating the digital transformation process and the usage of its services are the vulnerabilities to cyberspace, the advancement of technology and gadgets, and the employment of artificial intelligence in the development of current apps. In order to build cybersecurity governance that can be relied upon and is useful in accomplishing duties without hacking and tampering with data and information, it is necessary to implement simple programmes and tactics. This article emphasises the significance of cybersecurity governance in delivering secure and efficient technical means that can meet all risks and challenges and protect persons' data across all industries.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Rosário A. T. and Dias J. C., “Sustainability and the Digital Transition: A Literature Review,” Sustainability, vol.14, no.7, pp:1-18, March 2022. https://doi.org/10.3390/su14074072
Brauner P., Dalibor M., Jarke M., Kunze I., Koren I., et al., “A Computer Science Perspective on Digital Transformation in Production,” ACM Transactions on Internet of Things, vol.3, no.2, pp:1-32, May 2022. https://doi.org/10.1145/3502265
Agrawal S., Sahu A., and Kumar G., “A conceptual framework for the implementation of Industry 4.0 in legal informatics,” Sustainable Computing: Informatics and Systems, vol.33, pp:100650, January 2022. https://doi.org/10.1016/j.suscom.2021.100650
Mijwil M. M., Sadıkoğlu E., Cengiz E., and Candan H., “Siber Güvenlikte Yapay Zekanın Rolü ve Önemi: Bir Derleme,” Veri Bilimi, vol.5, no.2 pp:97-105, December 2022.
Mijwil M. M., Doshi R., Hiran K. K., Al-Mistarehi AH, and Gök M., “Cybersecurity Challenges in Smart Cities: An Overview and Future Prospects,” Mesopotamian journal of cybersecurity, vol.2022, pp:1-4, 2022. https://doi.org/10.58496/MJCS/2022/001
Ji-Young K., In L. J., and Gon K. K., “The All-Purpose Sword: North Korea's Cyber Operations and Strategies,” In Proceedings of International on Cyber Conflict, pp:1-6, Tallinn, Estonia, 28-31 May 2019. https://doi.org/10.23919/CYCON.2019.8756954
Oreyomi M. and Jahankhani H., “Challenges and Opportunities of Autonomous Cyber Defence (ACyD) Against Cyber Attacks,” In Blockchain and Other Emerging Technologies for Digital Business Strategies, pp:239–269, May 2022. https://doi.org/10.1007/978-3-030-98225-6_9
Welburn J., Grana J., and Schwindt K., “Cyber deterrence with imperfect attribution and unverifiable signaling,” European Journal of Operational Research, In press, July 2022. https://doi.org/10.1016/j.ejor.2022.07.021
Skopik F., Settanni G., and Fiedler R., “A problem shared is a problem halved: A survey on the dimensions of collective cyber defense through security information sharing,” Computers & Security, vol.60, pp:154-176, July 2016. https://doi.org/10.1016/j.cose.2016.04.003
Gunduz M. Z. and Das R., “Cyber-security on smart grid: Threats and potential solutions,” Computer Networks, vol.169, pp:107094, March 2020. https://doi.org/10.1016/j.comnet.2019.107094
Salem I. E., Mijwil M. M., Abdulqader A. W., Ismaeel M. M., Alkhazraji A., and Alaabdin A. M. Z., “Introduction to The Data Mining Techniques in Cybersecurity,” Mesopotamian Journal of Cybersecurity, vol.2022, pp:28-37, May 2022. https://doi.org/10.58496/MJCS/2022/004
Al Azzam S. B. N., “The AI algorithm for text encryption using Steganography,” Mesopotamian Journal of Cybersecurity, vol.2020, pp:18-27, 2020. https://doi.org/10.58496/MJCS/2022/003
Alwan A. H. and Kashmar A. H., “FCNN Model for Diagnosis and Analysis of Symmetric Key Cryptosystem,” Iraqi Journal For Computer Science and Mathematics, vol. 4, no. 1, pp. 53–61, November 2022. https://doi.org/10.52866/ijcsm.2023.01.01.006
Salem I. E., Salman A. M., and Mijwil M. M., “A Survey: Cryptographic Hash Functions for Digital Stamping,” Journal of Southwest Jiaotong University, vol.54, no.6, pp.1-11, December 2019. https://doi.org/10.35741/issn.0258-2724.54.6.2.
Sabah N., Sagheer A., and Dawood O., “Survey: (Blockchain-Based Solution for COVID-19 and Smart Contract Healthcare Certification),” Iraqi Journal For Computer Science and Mathematics, vol. 2, no. 1, pp. 1–8, January 2021. https://doi.org/10.52866/ijcsm.2021.02.01.001
Venkatraman S., Alazab M., and Vinayakumar R., “A hybrid deep learning image-based analysis for effective malware detection,” Journal of Information Security and Applications, vol.47, pp:377-389. August 2019. https://doi.org/10.1016/j.jisa.2019.06.006
Aggarwal, K., Mijwil, M. M., Sonia, Al-Mistarehi, AH., Alomari, S., Gök M., Alaabdin, A. M., and Abdulrhman, S. H., “Has the Future Started? The Current Growth of Artificial Intelligence, Machine Learning, and Deep Learning,” Iraqi Journal for Computer Science and Mathematics, vol.3, no.1, pp:115-123, January 2022. https://doi.org/10.52866/ijcsm.2022.01.01.013
Li S., Li Y., Han W., Du X.,Guizani M., and Tian Z., “Malicious mining code detection based on ensemble learning in cloud computing environment,” Simulation Modelling Practice and Theory, vol.113, pp:102391, December 2021. https://doi.org/10.1016/j.simpat.2021.102391
Capuano N., Fenza G., Loia V., Stanzione C.,” Explainable Artificial Intelligence in CyberSecurity: A Survey,” IEEE Access, vol.10, pp:93575 - 93600, September 2022. https://doi.org/10.1109/ACCESS.2022.3204171
Muhammad T. and Ghafory H., “SQL Injection Attack Detection Using Machine Learning Algorithm,” Mesopotamian journal of cybersecurity, vol.2022, pp:5-17, 2022. https://doi.org/10.58496/MJCS/2022/002
Mijwil M. M., Faieq A. K., and Al-Mistarehi AH., “The Significance of Digitalisation and Artificial Intelligence in The Healthcare Sector: A Review,” Asian Journal of Pharmacy, Nursing and Medical Sciences, vol.10, no. 3, pp: 25-32, November 2022. https://doi.org/10.24203/ajpnms.v10i3.7065
Mijwil M. M., Mutar D. S., Filali Y., Aggarwal K., and Al-Shahwani H., “Comparison Between Expert Systems, Machine Learning, and Big Data: An Overview,” Asian Journal of Applied Sciences, vol.10, no.1, pp:83-88, March 2022. https://doi.org/10.24203/ajas.v10i1.6930
Venu Y., “Cyber Security Policy considerations and Governance,” LinkedIn, 2018. https://www.linkedin.com/pulse/cyber-security-policy-considerations-governance-venu-yedugondla/
Mijwil M. M., Aggarwal K., Doshi R., Hiran K. K., Sundaravadivazhagan B. “Deep Learning Techniques for COVID-19 Detection Based on Chest X-ray and CT-scan Images: A Short Review and Future Perspective,” Asian Journal of Applied Sciences, vol.10, no.3, pp:224-231, July 2022. https://doi.org/10.24203/ajas.v10i3.6998
Ismael A. M. and Şengür A., “Deep learning approaches for COVID-19 detection based on chest X-ray images,” Expert Systems with Applications, vol.164, pp:114054, February 2021. https://doi.org/10.1016/j.eswa.2020.114054
Zhou T., Lu H., Yang Z., Qiu S., Huo B., and Dong Y., “The ensemble deep learning model for novel COVID-19 on CT images,” Applied Soft Computing, vol.98, pp:106885, January 2021. https://doi.org/10.1016/j.asoc.2020.106885
Jain R., Gupta M., Taneja S., and Hemanth D. J., “Deep learning based detection and analysis of COVID-19 on chest X-ray images,” Applied Intelligence, vol. 51, pp:1690-1700, October 2020. https://doi.org/10.1007/s10489-020-01902-1
Vaid S., Kalantar R., and Bhandari M., “Deep learning COVID-19 detection bias: accuracy through artificial intelligence,” International Orthopaedics, vol. 44, pp: 1539-1542, May 2020. https://doi.org/10.1007/s00264-020-04609-7
Wang L., Lin Z. Q., and Wong A.,” COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Scientific Reports, vol. 10, no.19549, pp:1-12, November 2020. https://doi.org/10.1038/s41598-020-76550-z
Mijwil M. M., Aggarwal K., Doshi R., Hiran K. K., and Gök M., “The Distinction between R-CNN and Fast R-CNN in Image Analysis: A Performance Comparison,” Asian Journal of Applied Sciences, vol.10, no.5, pp:429-437, November 2022. https://doi.org/10.24203/ajas.v10i5.7064
Fagbola T. M., Fagbola F. I., Aroba O. J., Doshi R., Hiran K. K., and Thakur S. C., “Smart Face Masks for Covid-19 Pandemic Management: A Concise Review of Emerging Architectures, Challenges and Future Research Directions,” IEEE Sensors Journal, pp:1-6, December 2022. https://doi.org/10.1109/JSEN.2022.3225067