Introduction
Cyber security has become an increasingly important issue in recent years, as the number of cyber-attacks has risen significantly. Cyber-attacks can cause significant damage to individuals, businesses, and governments, resulting in financial loss, disruption of services and the potential for personal data to be stolen. As such, organizations must ensure that their networks and systems are secure and protected from cyber-attacks. One of the most effective methods for improving cyber security is cloud computing.[1]. As a distributed computing method, “the cloud” facilitates online data storage and management for businesses. This innovation allows businesses to access their information and programs from any location, giving them the freedom to adapt to their ever-shifting requirements. In addition, cloud computing has various benefits in cyber security, including enhanced security, data protection, and cost savings.
Purpose Statement
This research proposal investigates how cloud computing can improve cybersecurity in the cyber domain. This research aims to analyze the benefits and challenges of using cloud computing and identify potential solutions. This research proposal will explore how cloud computing can reduce the risk of cyber-attacks, improve data security, and protect critical infrastructure.[2]. This research will also analyze how organizations can use cloud computing to enhance their overall security posture.
The Rationale for the Research
The advancement of technology has brought about a multitude of possibilities for data storage and processing. Cloud computing is one of the most popular business solutions, offering scalability, cost savings, and improved security. However, the cyber domain is not immune to the risk of cyberattacks, and organizations must ensure that their data remains secure.[3]. With the increasing sophistication of cyberattacks, organizations must understand how cloud computing can improve cybersecurity in the cyber domain.
Objectives of the Research
The objectives of this research are to:
- List the possible advantages of cloud computing for cybersecurity in the online world.
- Examine the challenges organizations face when using cloud computing for cybersecurity in the cyber domain.
- Analyze potential solutions to organizations’ challenges when using cloud computing for cybersecurity in the cyber domain.
Literature Review
This study proposal will conduct a literature assessment on the topic of cloud computing’s possible advantages in cyber security. The study’s scope includes the application of certain machine learning scenarios based on big data in cloud computing, cybersecurity risks, problems, and defensive mechanisms in cloud computing, as well as a survey of security concerns related to cloud computing.[4]. The potential advantages of cloud computing, the difficulties that companies confront when using cloud computing, and the possible solutions to these difficulties will all be discussed in the literature study. As a bonus, the study will go through how machine learning may be helpful and how crucial it is to have a secure cloud infrastructure. Finally, the assessment will look at the security risks, difficulties that cloud computing introduces, and the safeguards that may be put in place to deal with them.[5]. This literature review will give a holistic perspective on the advantages and disadvantages of employing cloud computing for cybersecurity in the cyber domain by synthesizing the research from various sources.
Cloud computing and machine learning are closely associated. According to Wu et al. (2019), their combination has become increasingly popular in big data scenarios[6]. That happens because machine learning needs computing resources and a significant amount of data to train models. Tweneboah-Koduah et al. (2017) evaluate that cloud computing offers the most appropriate way to process and keep a considerable amount of data[7]. Moreover, it offers a scalable and flexible infrastructure one can provision and de-provision as necessary without any difficulties.
There are numerous applications of machine learning in cloud computing. According to Wu et al. (2019), one of the most significant applications involves data collection and storage[8]. Organizations access large amounts of data to help them detect fraudulent activities. Tweneboah-Koduah et al. (2017) evaluate that cloud computing helps process and store significant data from many resources[9]. They can collect that data in real time and keep it in the cloud. Research by Raimundo & Rosário (2022) reveals that these institutions can utilize machine learning to evaluate the data[10]. Cloud computing can also offer the processing power to help train and run machine learning. Organizations can utilize Machine learning algorithms to assess data and see if there are patterns indicating fraudulent activity. Patel & Chudasama (2021) critically assess that institutions can also employ machine learning algorithms in assessing data and making predictions about future outcomes[11].
According to Wu et al. (2019), experts can also utilize machine learning algorithms to help assess hardware and determine the safety[12]. These algorithms help predict hardware failures before they occur. Organizations that recognize predictive maintenance’s significance experience increased efficiency because it helps cloud providers enhance availability and lower downtime. Research by Raimundo & Rosário (2022) reveals that people can use machine-learning algorithms to identify security threats.[13]. It helps cloud providers to protect data. Besides, it enhances security posture.
Akinsanya (2019) explains that cloud providers give organizations robust disaster recovery solutions[14]. Thus, they do not have to spend on expensive mechanisms when they experience disaster. Cloud providers guarantee data availability during service disruptions and disasters. Patel & Chudasama (2021) critically assess that they can also handle disaster recovery on behalf of the business[15]. Organizations should invest in cloud providers considering the many security mechanisms they offer. Abeshu & Chilamkurti (2018) explain that they also have teams of security professionals who ensure no security threats[16]. They achieve this goal by constantly monitoring the cloud environment. Businesses benefit from the expertise and the security mechanisms because they offer an increased level of security. Patel & Chudasama (2021) evaluate that businesses can never achieve this level of security without the expertise and mechanisms[17].
Akinsanya (2019) explains that many cybersecurity risks are related to cloud computing[18]. For example, one can lose data or experience system failure due to malicious software. Malware can infect cloud-based systems leading to these problems. Systems can also become unavailable because of Denial-of-service (DoS) attacks. Patel & Chudasama (2021) critically assesses that attacks use them to target cloud service providers[19]. Individuals who can access cloud-based data can also compromise security. Research by Raimundo & Rosário (2022) reveals that they can unintentionally or deliberately download malware or share confidential information[20]. Hackers can also cause data breaching when they access cloud-based data. Abeshu & Chilamkurti (2018) explain that this unauthorized access can happen through insecure network configurations, unpatched software, and weak passwords[21].
Akinsanya (2019) explains that with the increasing adoption of cloud computing security, concerns remain a critical issue[22]. For example, overreliance on a particular cloud service provider can make it challenging to change. Tweneboah-Koduah et al. (2017) evaluate that organizations must ensure they cloud providers who provide flexibility and enables easy switch[23]. Organizations may also lack appropriate disaster recovery methods. Thus, it becomes difficult to guarantee that data is available during disruptions. Research by Raimundo & Rosário (2022) reveals that institutions may lack proper security controls to prevent unauthorized access, which leads to breaching[24].
Patel & Chudasama (2021) critically assesses that cloud-computing also presents several unique challenges to security, such as shared accountability for ensuring that cloud-based data remains safe[25]. It can confuse when those involved must identify who is responsible for who should execute particular security measures. Akinsanya (2019) explains that cloud service providers should also abide by standards and regulations associated with data privacy and security[26]. Ensuring compliance can be challenging and complex.
Organizations can execute various defensive mechanisms to deal with the cybersecurity risks and problems associated with cloud computing security. Research by Raimundo & Rosário (2022) reveals that regular analysis of cloud-based data and systems can help detect gaps and vulnerabilities in security controls[27]. Regularly monitoring and auditing cloud infrastructure allow organizations to identify vulnerabilities and security breaches. Abeshu & Chilamkurti (2018) explain that the organization must provide the security teams with the tools to help monitor cloud services and see if there are any suspicious activities[28]. Cloud providers must also guarantee regular updates of software and security patches as a safeguard to deal with the risks associated with cloud computing. It helps address any unknown vulnerabilities. Patel & Chudasama (2021) critically assesses that it also helps safeguard cloud infrastructure and data from new security vulnerabilities and threats[29].
Patel & Chudasama (2021) explain that Organizations can minimize the effects of security incidents through the regular backup of cloud-based systems and data[30]. They should couple the backup with a robust disaster recovery plan. Tweneboah-Koduah et al. (2017) evaluate that organizations can also detect and respond to threats fast through real-time logging and monitoring of cloud-based systems and data[31]. Encrypting data also ensures those who are not supposed to access the data do not. Akinsanya (2019) explains that they should use to protect data at rest and in transit[32]. For example, organizations can use advanced encryption standards to be safe from hackers.
Research by Raimundo & Rosário (2022) reveals that access control mechanisms and strong authentication are also vital safeguards that organizations can employ to deal with security risks and difficulties that cloud computing introduces[33]. Their implementations ensure that only unauthorized people do not access sensitive applications and data. Patel & Chudasama (2021) critically assesses that they should also develop business continuity and disaster recovery plans to guarantee that cloud services remain operational and available even when the institution encounters crises or security breach[34]. The plans can involve recovery and backup procedures. Akinsanya (2019) explains that procedures for restoring data and services during disasters guarantee that in case of unfortunate events, the organization remains standing[35].
Patel & Chudasama (2021) evaluates that compliance can also help to deal with these security risks and difficulties due to cloud computing[36]. Various regulatory requirements, including HIPAA and GDPR, require organizations to execute proper security controls and regularly check to ensure no potential breaches. Tweneboah-Koduah et al. (2017) evaluate that cloud providers should abide by these requirements to help deal with these threats[37]. For example, ISO 27001 is a compliance framework organizations can use to guarantee that cloud providers observe the requirements and best practices to help improve security.
Patel & Chudasama (2021) evaluate that organizations can also segregate data to ensure data safety. Segregation protects data from unauthorized access[38]. Organizations should keep data in separate environments. It guarantees that data in another environment remains safe if an intruder compromises one environment. Research by Raimundo & Rosário (2022) reveals that these organizations must also guarantee that they isolate the environments from each other to enhance safety[39].
There are many challenges organizations experience when adopting cloud computing. Raimundo & Rosário (2022) reveals that one of the most common challenges related to using this technology involves guaranteeing cloud providers abide by their governance policies[40]. Cloud services must have efficient controls to manage and monitor their cloud usage. Moreover, they should be auditable. Abeshu & Chilamkurti (2018) explain that ensuring compliance with these policies can be challenging[41]. The best way to address this problem involves using Cloud Management tools to help in monitoring cloud services. Patel & Chudasama (2021) explains that cloud management platforms can help in this situation adopting cloud governance frameworks to ensure that cloud services execute appropriate controls and policies to guarantee compliance is also crucial[42]. One of these frameworks is the Cloud Security Alliance’s Cloud Control Matrix.
Abeshu & Chilamkurti (2018) explain that another challenge organizations can experience when using cloud computing is an increased cost[43]. A lack of proper monitoring of cloud service usage can lead to unexpected costs. Inaccuracy in pricing models can also lead to increased costs. Patel & Chudasama (2021) critically assess that organizations must factor in the expense involved in the movement of data[44]. Moreover, during the calculations of the cloud services costs, they should include all ancillary services costs. Research by Raimundo & Rosário (2022) reveals that they should closely monitor their cloud services usage to guarantee transparent pricing models[45]. They can use cost management devices to estimate and track their cloud costs. They can optimize cloud services to lower expenses.
Organizations may also experience interoperability problems and data silos. Akinsanya (2019) explains that that happens when they utilize varying cloud providers for different services[46]. Abeshu & Chilamkurti (2018) explain that the best way to deal with this problem involves employing standard-based approaches to guarantee that the existing applications and systems are compatible with the cloud services[47]. Cloud brokers also help facilitate interoperability. They offer a common interface for many cloud services.
Patel & Chudasama (2021) critically assesses that another problem related to cloud computing usage is data privacy[48]. Failure to abide by the data protection regulations during processing and data storage can significantly threaten privacy. Research by Raimundo & Rosário (2022) reveals that organizations should adopt data access controls and encryption to enhance privacy[49]. They should also work with cloud providers that abide by data protection regulations. Patel & Chudasama (2021) evaluates that data classification policies help identify and treat sensitive data appropriately[50].
Tweneboah-Koduah et al. (2017) evaluate that security issues resulting from shared responsibility are a significant concern for organizations regarding cloud computing[51]. Customers secure their applications and data. On the other hand, cloud providers secure the infrastructure. This shared responsibility can result in gaps in security because people may not know what to handle and what to leave out. Research by Raimundo & Rosário (2022) reveals that it leads to an increased risk of cyber threats such as data breaches[52]. Organizations can solve this problem by executing appropriate security measures, including monitoring and access controls. Abeshu & Chilamkurti (2018) explain that strong security measures and frameworks can help address this issue[53].
Statement of the Problem
The prevalence of cyber-attacks in the digital domain is increasing alarmingly. Organizations becoming increasingly reliant on digital technologies such as cloud computing become more vulnerable to cyber threats. To ensure the security of their data and systems, organizations must develop practical solutions to mitigate the risks associated with cloud computing.
Justification of the Study
The use of cloud computing in the cyber domain is becoming increasingly common as organizations look for ways to improve their security. This study seeks to understand how cloud computing can improve cybersecurity and the challenges organizations face when using cloud computing. By understanding these issues, organizations can develop strategies to improve their cybersecurity posture and protect their data from malicious actors. Furthermore, this research will provide useful insights into the current status of cloud computing security and the prospective solutions that may be adopted to increase security in the cyber domain.
Major Assumptions/Hypotheses
This study’s main presumption is that cloud computing can enhance cybersecurity in cyberspace. Cloud computing can help businesses cut costs, boost productivity, and offer better defence against cyberattacks. Additionally, cloud computing can give businesses better access to and control over their data, enabling them to secure their networks and system more effectively[54]. Additionally, it is assumed that businesses must be conscious of the potential security risks linked to cloud computing and devise plans to reduce those risks.
Limitations to the Study
The main limitation of this study is that it is limited to the use of cloud computing in improving cybersecurity in the cyber domain. That means that other technologies and approaches to cybersecurity, such as data encryption and access control, are not considered. Additionally, the study is limited to the literature available in English, so any relevant research conducted in other languages may be excluded.[55]. Finally, the study is limited to the current state of cloud computing technology and its associated security challenges, so any changes to the technology or challenges in the future will not be considered.
Conclusion
In conclusion, cloud computing can improve cybersecurity in the cyber domain. This research proposal identified the potential benefits of cloud computing, the challenges faced when using cloud computing, and potential solutions. Cloud computing can increase the effectiveness and efficiency of cybersecurity systems. Organizations must take precautions to reduce any possible security threats related to cloud computing. With the right tools and strategies in place, organizations can achieve improved levels of cybersecurity in the cyber domain.
List of References
Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
Akinsanya, O. O., Papadaki, M., & Sun, L. (2019, March). Current cybersecurity maturity models: How effective in the healthcare cloud? In CERC (pp. 211-222). https://doi.org/10.1108/ICS-05-2019-0060
Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95, 169-185. https://link.springer.com/article/10.1007/s11277-017-4434-6
Wu, M., Song, Z., & Moon, Y. B. (2019). Detecting cyber-physical attacks in Cyber Manufacturing systems with machine learning methods. Journal of intelligent manufacturing, 30, 1111-1123. https://link.springer.com/article/10.1007/s10845-017-1315-5
[1] Akinsanya, O. O., Papadaki, M., & Sun, L. (2019, March). Current cybersecurity maturity models: How effective in the healthcare cloud? In CERC (pp. 211-222). https://doi.org/10.1108/ICS-05-2019-0060
[2] Wu, M., Song, Z., & Moon, Y. B. (2019). Detecting cyber-physical attacks in Cyber Manufacturing systems with machine learning methods. Journal of intelligent manufacturing, 30, 1111-1123. https://link.springer.com/article/10.1007/s10845-017-1315-5
[3] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[4] Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95, 169-185. https://link.springer.com/article/10.1007/s11277-017-4434-6
[5] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[6] Wu, M., Song, Z., & Moon, Y. B. (2019). Detecting cyber-physical attacks in Cyber Manufacturing systems with machine learning methods. Journal of intelligent manufacturing, 30, 1111-1123. https://link.springer.com/article/10.1007/s10845-017-1315-5
[7] Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95, 169-185. https://link.springer.com/article/10.1007/s11277-017-4434-6
[8] Wu, M., Song, Z., & Moon, Y. B. (2019). Detecting cyber-physical attacks in Cyber Manufacturing systems with machine learning methods. Journal of intelligent manufacturing, 30, 1111-1123. https://link.springer.com/article/10.1007/s10845-017-1315-5
[9] Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95, 169-185. https://link.springer.com/article/10.1007/s11277-017-4434-6
[10] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[11] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[12] Wu, M., Song, Z., & Moon, Y. B. (2019). Detecting cyber-physical attacks in Cyber Manufacturing systems with machine learning methods. Journal of intelligent manufacturing, 30, 1111-1123. https://link.springer.com/article/10.1007/s10845-017-1315-5
[13] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[14] Akinsanya, O. O., Papadaki, M., & Sun, L. (2019, March). Current cybersecurity maturity models: How effective in the healthcare cloud? In CERC (pp. 211-222). https://doi.org/10.1108/ICS-05-2019-0060
[15] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[16] Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
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[19] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[20] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[21] Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
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[23] Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95, 169-185. https://link.springer.com/article/10.1007/s11277-017-4434-6
[24] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[25] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[26] Akinsanya, O. O., Papadaki, M., & Sun, L. (2019, March). Current cybersecurity maturity models: How effective in the healthcare cloud? In CERC (pp. 211-222). https://doi.org/10.1108/ICS-05-2019-0060
[27] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[28] Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
[29] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[30] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[31] Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95, 169-185. https://link.springer.com/article/10.1007/s11277-017-4434-6
[32] Akinsanya, O. O., Papadaki, M., & Sun, L. (2019, March). Current cybersecurity maturity models: How effective in the healthcare cloud? In CERC (pp. 211-222). https://doi.org/10.1108/ICS-05-2019-0060
[33] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[34] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[35] Akinsanya, O. O., Papadaki, M., & Sun, L. (2019, March). Current cybersecurity maturity models: How effective in the healthcare cloud? In CERC (pp. 211-222). https://doi.org/10.1108/ICS-05-2019-0060
[36] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[37] Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95, 169-185. https://link.springer.com/article/10.1007/s11277-017-4434-6
[38] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[39] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[40] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[41] Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
[42] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[43] Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
[44] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[45] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[46] Akinsanya, O. O., Papadaki, M., & Sun, L. (2019, March). Current cybersecurity maturity models: How effective in the healthcare cloud? In CERC (pp. 211-222). https://doi.org/10.1108/ICS-05-2019-0060
[47] Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
[48] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[49] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[50] Patel, K., & Chudasama, D. (2021). National security threats in cyberspace. National Journal of Cyber Security Law, 4(1), 12-20p.
[51] Tweneboah-Koduah, S., Skouby, K. E., & Tadayoni, R. (2017). Cyber security threats to IoT applications and service domains. Wireless Personal Communications, 95, 169-185. https://link.springer.com/article/10.1007/s11277-017-4434-6
[52] Raimundo, R. J., & Rosário, A. T. (2022). Cybersecurity in the internet of things in industrial management. Applied Sciences, 12(3), 1598. https://doi.org/10.3390/app12031598
[53] Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
[54] Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
[55]Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169-175. https://doi.org/10.1109/MCOM.2018.1700332
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