Critical cybersecurity issues faced by employees during remote work
Cybersecurity challenges faced by employees during remote working and the process of mitigating them
Cybersecurity is a broad topic consisting of a wide array of topics that need to be narrowed down for further research in this context; in order to do so, focus on the topic mentioned above has been kept. As per the ideas of Sarker et al. (2020), cybersecurity attacks have increased since the initiation of remote work due to the pandemic. Mostly, issues regarding data exfiltration packages have significantly increased as 55% of the attacks are of this type. Additionally, critical issues are phishing emails, account takeovers, malware downloads, ransomeware and application targeted tasks. Furthermore, challenges associated with the unauthorised transfer or data removal from a device through phishing emails, malware, as well as perpetrators can be considered as a key challenge that needs to be resolved as remote working has become an investable element of work that will be continued in future.
Figure 1: Critical issues in Cybersecurity due to remote working
(Source: Sarker et al. 2020)
Cybersecurity can be contemplated as a core element of computer science as it helps to improve the field of study as well as programs in the contemporary computer-reliant world (Pranggono and Arabo 2021). In essence, cybersecurity can be considered as a subset of general aspects of computer science as it talks about the underlying security parameters of computers, networks as well as peripheral devices. Therefore, it can be indicated that the chosen topic essentially integrates with computer science in general.
The underlying research question that will further drive this study can be defined as follows:
What are the key cybersecurity challenges employees face during remote working, and how can an individual mitigate them?
In order to further investigate this question, a mixed-method study with primary quantitative and qualitative data analysis methods can be carried out with the IT professionals who have faced similar issues during the remote working period. Primary quantitative data analysis processes a clear idea on a particular topic by collecting first-hand data from a target population to develop a clear insight into the chosen topic area of cybersecurity (Lezzi, Lazoi and Corallo 2018). Additionally, primary qualitative data can further support or discard specific findings that may help to understand the underlying fallacies in a specific topic area. Therefore, in this project, focusing on primary a mixed-method of data collection can be considered as an important parameter for defining the accurate areas from an employee’s perspective as well as from an employer’s perceptual views.
As the study intends to explore mitigation actions for the cyber-security issues found in the discussing context, evaluating the issues and finding out best practices to resolve them can be considered relevant for improving the loopholes. Additionally, remote work is expected to be a new normal paradigm in the near future, which means employees and employers need to be prepared for the cybersecurity parameters (Lallie et al. 2021). Through the proposed study, a clear idea of the core cybersecurity issues can be eventually derived, and the contributions of the study will be helpful for organisations that are continuing to plan remote working for resource crunch. The study does not have any negative implications; however, it has limitations regarding the timeframe as well as costs required to conduct a primary data analysis which may slightly alter the results.
The importance of cybersecurity in computer science
Human-Robot Interaction (HRI) can be considered as a field of study that helps understand, evaluating as well as robotic design systems to integrate with human works (Chakraborty et al. 2018). The recent growth in human-robot interaction will be defined through a short report.
HRI can be of two categories, including proximate interaction as well as remote interaction (Xu 2019). As for the applications that require mobility without the help of human interaction, social interaction or physical manipulation, mobile or bipedal robots can be used. On the other hand, for completing co-located tasks, proximate interaction must be emphasised. For example, service robots can be considered as a significant example of proximate HRI. Additionally, HRI can assist in social interaction scaffolding, emotional comfort, physical comfort, behavioural modelling, and other computing areas. Research question in the discussing context can be defined as follows:
- How HRI can be further improved for proper computational growth?
Through an exploratory research method, this specific question can be done.
In conclusion, it can be defined that HRI is still in its infancy, and it may further create challenges for its users. Additionally, consistency maintenance, influencing actual human interaction and other related aspects may be found. However, with significant growth in technology, a concise development approach is expected for further improvement.
Alongside significant growth in research proceedings, significant strategic growth in different research areas is expected in computer science in the upcoming decades (Lopes et al. 2020). Additionally, computer science is expected to improve from different horizons with the latest trends spotted in the future.
The significant research areas that will further improve the field of computer science include Artificial Intelligence (AI), robotics, Machine Learning (ML), big data analytics, computer-assisted education, cyber-security and bio-informatics (Schuh, Scholz and Patzwald 2019). On the other hand, multiple types of research have defined that Natural Language Processing, Blockchain, Computer vision establishment, edge computing and quantum computing are some critical topics that may take over the market to a greater extent. In addition, researchers have defined that semantic web, image processing, augmented reality, and human-computer interactions are some crucial areas that will further improve market performance (Lopes et al. 2020). Exponential growth over recent years in the field of cybersecurity defines a steady demand increment which is expected to storm the technology industry.
Computer science is one of the vast fields that sheds light on several fields of science and engineering, which indicates that different areas of computer science will thrive in the upcoming time. The most common technological growth is expected in the areas of AI, ML, HCI, augmented reality and big data-related topics in the near future. Further research on biomechanics and quantum computing is expected to improve the overall growth parameters of the study.
References
Chakraborty, B.K., Sarma, D., Bhuyan, M.K. and MacDorman, K.F., 2018. Review of constraints on vision-based gesture recognition for human–computer interaction. IET Computer Vision, 12(1), pp.3-15.
Lallie, H.S., Shepherd, L.A., Nurse, J.R., Erola, A., Epiphaniou, G., Maple, C. and Bellekens, X., 2021. Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic. Computers & Security, 105, p.102248.
Lezzi, M., Lazoi, M. and Corallo, A., 2018. Cybersecurity for Industry 4.0 in the current literature: A reference framework. Computers in Industry, 103, pp.97-110.
Lopes, J.A.P., Madureira, A.G., Matos, M., Bessa, R.J., Monteiro, V., Afonso, J.L., Santos, S.F., Catalão, J.P., Antunes, C.H. and Magalhães, P., 2020. The future of power systems: Challenges, trends, and upcoming paradigms. Wiley Interdisciplinary Reviews: Energy and Environment, 9(3), p.e368.
Pranggono, B. and Arabo, A., 2021. COVID?19 pandemic cybersecurity issues. Internet Technology Letters, 4(2), p.e247.
Sarker, I.H., Kayes, A.S.M., Badsha, S., Alqahtani, H., Watters, P. and Ng, A., 2020. Cybersecurity data science: an overview from machine learning perspective. Journal of Big data, 7(1), pp.1-29.
Schuh, G., Scholz, P. and Patzwald, M., 2019, October. Technological trends in context of industry 4.0 and their industrial applications. In 2019 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS) (pp. 1-6). IEEE.
Xu, W., 2019. Toward human-centered AI: a perspective from human-computer interaction. interactions, 26(4), pp.42-46.
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