CSIS 690 Liberty IoT Devices in Homes Has Caused Interoperability Issue Discussion – Description
Post 1:
Sean Fitzgerald
Problem Statement:
Internet of Things (IoT) devices have become increasingly popular and have created congestion with the interoperability between the gateway and the device inside home networks. To fix this, the router should have a protocol that limits the amount of congestion that one device can produce on the network. This will limit the number of packets an IoT device can produce in a particular set of time.
Research Question:
Will implementing a new packet-limiting protocol improve the interoperability between the gateway and IoT devices, considering the role of device type and manufacturer protocols?
Article 1:
Authors: Abhishek Khanna & Sanmeet Kaur
Title: Internet of Things (IoT), Applications and Challenges: A Comprehensive Review
Citation: Khanna, A., & Kaur, S. (2020). Internet of Things (IoT), Applications and Challenges: A Comprehensive Review. Wireless Personal Communications, 114(2), 1687–1762. https://doi.org/10.1007/s11277-020-07446-4
Gaps: The article talks about the concepts of IoT such as an overview of the evolution of the devices and talks about the potential future integration of Artificial Intelligence (AI) into the IoT infrastructure. However, there is a lack of literature about the interoperability and standardization of IoT.
Recommendations: The authors recommend that as IoT devices continue to proliferate there would need to be standardization of the protocols used and frameworks to ensure interoperability among the vast amounts of IoT devices. Thus, future research could help investigate the development and adoption of standards that could create better data exchanges between IoT devices.
Article 2:
Authors: Moussa Aboubakar, Mounir Kellil, & Pierre Roux
Title: A review of IoT network management: Current status and perspectives
Citation: Aboubakar, M., Kellil, M., & Roux, P. (2022). A review of IoT network management: Current status and perspectives. Journal of King Saud University – Computer and Information Sciences, 34(7), 4163–4176. https://doi.org/10.1016/j.jksuci.2021.03.006
Gaps: The gaps in this literature refer to network performance problems due to a variety of different reasons. Some of these include error-prone communication and poor radio channels. Another gap is the lack of efficient management for IoT networks, which would be needed to ensure decent network performance.
Recommendation: One of the recommendations of this article is to continue research on improved network management protocols to make network traffic more efficient with IoT devices. According to the article, they should focus on resource-constrained devices and error-prone communication channels. One of the other recommendations is to enhance the `discovery of different IoT devices which could include device configuration, discovery, monitoring, and control of IoT devices on a network. Lastly, the authors talk about the need for future research into standardization and interoperability as there is a vastly large number of different manufacturers of IoT devices with no set standard or framework. With more research into this, it could allow for more seamless integration with IoT devices on a network.
Article 3:
Authors: N.N. Srinidhi, S.M. Dilip Kumar, & K.R. Venugopal
Title: Network optimizations in the Internet of Things: A review
Citation: Srinidhi, N. N., Kumar, S., & Venugopal, K. R. (2019). Network optimizations in the Internet of Things: A review. Engineering Science and Technology, an International Journal, 22(1), 1–21. https://doi.org/10.1016/j.jestch.2018.09.003
Gaps: There are a few gaps in this literature that were not focused on but the primary one for this study was the gap in IoT networks requiring optimized routing. IoT devices require a vast amount of data to ensure structured communication between the gateway and other devices.
Recommendations: This article talked about future research needed in congestion management where there can be a technique or protocol that can handle large amounts of data generated by IoT devices. Along with this, there also would need to be work in standardization and interoperability of IoT devices to achieve better integrations among the gateway and the diverse IoT devices.
Article 4:
Authors: Dylan Kauling, May AlTaei, & Qusay H. Mahmoud
Title: Impact of IoT device saturation on home WiFi networks
Citation: Kauling, D., AlTaei, M., & Mahmoud, Q. (2018, April 1). Impact of IoT device saturation on home Wi-Fi networks. IET Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/document/8643192/metri…
Gaps: This article had gaps regarding limited research on the impacts of always-on IoT devices on a home wireless network. Insufficient exploration of how IoT devices can impact a router and network performance. One of the other gaps is the discrepancies with different routers when they were subjected to the increasing number of active IoT devices.
Recommendations: The authors recommend testing additional smart devices to determine their impact on network performance and find what devices if any require more data to be transmitted. Doing this future research will help with understanding and performance of consumer-grade routers with large amounts of IoT devices.
Article 5:
Authors: Ryan Lattrel
Title: Designing and Implementing an Application Layer Network Protocol Using UNIX Sockets and TCP.
Citation: Lattrel, R. (2012, November 16). Designing and Implementing an Application Layer Network Protocol Using UNIX Sockets and TCP. https://www.egr.msu.edu/classes/ece480/capstone/fa… to an external site.
Gaps: This article does not provide a direct gap in my current focus for the problem statement; however, it is valuable for this research as it provides guidance and evidence for solving the existing problem with future recommendations.
Recommendations: This article refers to implementing a highly versatile protocol that could be modified to any program’s needs. This shows how a code could be written in order to produce the desired packet-limiting protocol as it meets most of the requirements and can be used for future studies.
Post 2:
Shandi Buckner
Shandi Buckner
Analyzing the Effects of Virtual Reality on the Healthcare Sector
Lack of Research on the Benefit of VR in Healthcare
Virtual reality, often referred to as the metaverse, has swiftly taken society by storm. Virtual reality allows for users to step outside of actual reality and step into a world with endless backdrops and experiences through virtual reality. Virtual reality usually consists of a headset with handheld controllers or interactive gloves that can stimulate a unique environment and portray the users’ actual movements into that environment either through a first person or third person view. Virtual can be defined as “being in essence but not in fact” and reality as “a state or quality of being real.” Since virtual worlds can be created through virtual reality, this can allow creators to design virtual products, environments, and experiences. Since virtual reality is a relatively new concept, the issue with this is that there is little research done on the effects of using virtual reality and VR assisted tools. Bu et el. (2021) states that virtual reality (VR) technology has a great benefit as it allows the combination of manufacturing and information technology. It is important to study the effects of this in multiple sectors but most particularly, the healthcare sector could greatly benefit through having a virtual reality platform or have products that are assisted by the use of virtual reality. Bu et el. (2021) states that VR assisted healthcare products offer increased prevention, rehabilitation, and palliative care services to patients. Unfortunately, due to the lack of consistent of VR and VR supported tools in healthcare, there is no simple way to track the benefits of VR in healthcare. “Demonstrating significant improvement in healthcare outcomes using the metaverse will be difficult to prove” (Ganapathy, 2022). In an article written by nursing students, Saap et el. (2022) writes that VR in healthcare can provide unique benefits such as reaching a group of people in need of care who would otherwise be difficult to reach via traditional measures. This would be particularly helpful for patients who need at-home care and are unable to travel to a hospital for treatment.
3.1 Peer Feedback
Based on peer feedback, there seems to be skepticism around VR in healthcare and also a feeling of uneasiness from wondering how VR affects our abilities to form relationships in real life. I do agree that too much time spent in VR can create social issues. It should be stated that this VR will only be used in healthcare when deemed medically necessary. This also makes me wonder how long is too long to spend in a session and when we could possibly see the social challenges stemming from spending excess amounts of time in a virtual reality setting. When researching this topic, I actually stumbled across something that goes against my person views which I think is worth considering. There is a study done on dementia patients who are losing their social skills to re-adapt them to socialization without the pressure of actually conversing with someone by simulating this through virtual reality. Sun et el. (2020) states that the results of this study showed several benefits such as seeing what interactions are meaningful for the patient, increased confidence in patients, and increased usage of reminiscence. When thinking of the benefits of this to the study group of patients with dementia, the benefits of VR especially in patients with impaired mental and physical capabilities outweigh any negatives. In fact, VR can actually improve the lives of those with social barriers.
In adults with a more sedentary lifestyle, gaining muscle strength back through VR could be a great option. In a study by Prasertsakul et el. (2018), Prasertsakul states that within the group of adults studied with a sedentary lifestyle, they focused on the benefits of using VR to help with motor learning and postural control. Prasertsakul et el. (2018) also states that they found that VR assisted exercises improved postural control and motor learning more than traditional exercise techniques. “Many factors, such as the familiarity in the virtual environment, anticipation of required movements, and the improvement of sensorimotor, coordination and balance, add to the increasing score while practicing with this program for a long time” states Prasertsakul et el. (2018) on the benefits of using VR over traditional exercise methods.
Another subject of peer feedback was how virtual reality can be successfully implemented and adapted into the healthcare setting. Since this is a relatively new concept to the healthcare field, there should be a study done on the costs and time needed to implement virtual reality into a medical facility. Abdelmaged (2021) highlights some of the challenges with implementing VR by saying that there are concerns of lack of knowledge in using VR and choosing a VR software that meets the healthcare industry standards. To circumvent this, I would suggest that healthcare professionals and the creators of medical VR application and tools work together closely so that the applications and software are designed through the lens of a doctor. This also ensure that there will be people in the medical field that have seen this created and know how it works, so I would also suggest that there be formal trainings on using VR for all doctors implementing it into their practice. Chung et el. (2023) took a look at the implications of implementing virtual reality into the healthcare sector and concluded their study with a positive outlook. They state that formal training is the top objective for successfully implementing VR, and they also state that clinical guidelines, manuals, and training workshops would be an added benefit for all providers. I believe there are several ways that VR can be successfully implemented into healthcare.
3.2 Aim and Objectives
The aim of this project is to develop an application that takes patient input and data from their virtual reality sessions and displays this information for their healthcare professionals. Kouijzer et el. (2023) states that despite potential value to healthcare, virtual reality in healthcare is still in its infancy and has taken longer to implement on a large scale. Kouijzer et el. (2023) also states that in order for VR to better suit the healthcare sector, there must be standardization in implementation as well as strategies to use the data from VR to help doctors, patients, and companies. This application will help aid healthcare professionals who are among the first to adapt virtual reality to their practice with assistance in tracking patient information such as their progress and response to their virtual reality treatments, making better decisions regarding their patient’s care, and supporting the advancement of the healthcare environment. This information will be tracked and recording on the application. For this application, the patient’s response to their virtual reality treatment will be given in a percentage. The goal for this application is to also highlight areas that are responding well in treatment and places where there can be improvement. This will help give healthcare professionals the data they need to better tailor their simulated VR environments and tools.
To achieve the above aim, the following objectives would be addressed during the project:
To develop a user-friendly GUI that allows healthcare professionals to easily see how their patient is responding to their virtual reality treatment
To integrate patient data and analyze it for decision making about their care
To record patient progress and monitor this data against patients’ goals
To have a database management system to store patient information for future use and also for the advancement of using virtual reality in the healthcare sector
3.3 Recommendations
Kouijzer et el. (2023) recommends that in order for VR to better suit the healthcare sector, there must be standardization in implementation as well as strategies to use the data from VR to help doctors, patients, and companies. This application will serve as a basis for a framework to be built upon to better support VR in healthcare. Chung et el. (2023) recommends that for successful implementation of VR, healthcare professionals should develop training modules, clinical guidelines, and product manuals. These resources would help promote a better outcome of the implementation and also provide standardization around a new concept to both technology and healthcare.
3.4 Research Question(s) – Revised due to peer feedback
What are the effects, benefits, and challenges of virtual reality in healthcare?
Can VR be harmful to our social capabilities?
How can VR be especially beneficial to those who are handicapped either mentally or physically?
How secure is virtual reality?
3.5 Methodology
I will be using SQL programming language to create a GUI for a database management system to display the outcomes of using virtual reality in healthcare. This GUI will display the patient’s personal information, their scheduled virtual reality tasks, their progress to completion, and which tasks the patients excels in and/or needs improvement. SQL is being used as it is relatively simple to design a GUI using this platform. A GUI, or graphical user interface, is a visualization of data being recorded. This visualization will help aid in the determination of any benefit of virtual reality in healthcare. Lee et el. (2023) states GUIs are preferred over text-based approaches due to better understanding and satisfaction with content layout, images, navigation, and aesthetics. Lee et el. (2023) also states that GUIs are especially helpful for novice to low-literacy user as the extensive graphics help provide a better understanding.
3.6 Expected Product and Model
The expected product will be a database management system presented as a GUI using SQL. There will be two actors – patient and doctor. The doctor will have access to the back-end system and will be able to update information stored within the database such as assigning tasks to patients, recording the results of the task, and viewing the patient’s information such as tasks completed and any notes or comments. The patient will have a limited view and only have access to the front-end system which will display their patient information, VR tasks completed, and any notes or feedback from the doctor on their task. Both doctor and patient will have a login. This will help provide extra security as the patient does not need access to the back-end system. This will prevent any unwanted access to sensitive data of the patients. Below is the use case and functions of the application:
REFERENCES
Abdelmaged, M. A. M. (2021). Implementation of virtual reality in healthcare, entertainment, tourism, education, and retail sectors. (). St. Louis: Federal Reserve Bank of St Louis.
Bu, L., Chen, C., Ng, K. K. H., Zheng, P., Dong, G., & Liu, H. (2021). A user-centric design approach for smart product-service systems using virtual reality: A case study. Journal of Cleaner Production, 280, 124413. https://doi.org/10.1016/j.jclepro.2020.124413 site.
Chung, O. S., Dowling, N. L., Brown, C., Robinson, T., Johnson, A. M., Ng, C. H., Yücel, M., & Segrave, R. A. (2023). Using the theoretical domains framework to inform the implementation of therapeutic virtual reality into mental healthcare. Administration and Policy in Mental Health and Mental Health Services Research, 50(2), 237-268. https://doi.org/10.1007/s10488-022-01235-w
Ganapathy, K. (2022). Metaverse and healthcare: A clinician’s perspective. Apollo Medicine, 19(4), 256-261. https://doi.org/10.4103/am.am_103_22
Kouijzer, Marileen M T E, Kip, H., Bouman, Y. H. A., & Kelders, S. M. (2023). Implementation of virtual reality in healthcare: A scoping review on the implementation process of virtual reality in various healthcare settings. Implementation Science Communications, 4(1), 67-67. https://doi.org/10.1186/s43058-023-00442-2
Lee, M., Kang, D., Joi, Y., Yoon, J., Kim, Y., Kim, J., Kang, M., Oh, D., Shin, S., & Cho, J. (2023). Graphical user interface design to improve understanding of the patient-reported outcome symptom response. PloS One, 18(1), e0278465-e0278465. https://doi.org/10.1371/journal.pone.0278465
Prasertsakul, T., Kaimuk, P., Chinjenpradit, W., Limroongreungrat, W., & Charoensuk, W. (2018). The effect of virtual reality-based balance training on motor learning and postural control in healthy adults: A randomized preliminary study. Biomedical Engineering Online, 17(1), 124-124. https://doi.org/10.1186/s12938-018-0550-0
Saab, M. M., Landers, M., Murphy, D., O’Mahony, B., Cooke, E., O’Driscoll, M., & Hegarty, J. (2022). Nursing students’ views of using virtual reality in healthcare: A qualitative study. Journal of Clinical Nursing, 31(9-10), 1228-1242. https://doi.org/10.1111/jocn.15978
Sun, W., Horsburgh, S., Quevedo, A., Liscano, R., Tabafunda, A., Akhter, R., Lemonde, M., Kapralos, B., Tokuhiro, A., Bartfay, E., & Ashtarieh, B. (2020). Advancing reminiscence therapy through virtual reality application to promote social con-nectedness of persons with dementia. Gerontechnology, 19(s), 1-1. https://doi.org/10.4017/gt.2020.19.s.70041
Post 3:
David Healy
David Healy
The Impact of artificial intelligence on network security
Exploring the role of artificial intelligence in the detection, prevention, and mitigation of cyber threats in enterprise networks.
Artificial intelligence (AI) is the process of making machines intelligent, to think for themselves, learn from past events, understand and act based on the information obtained (Iyer and Umadevi, 2023). AI has been a dramatic improvement over the conventional methods of detecting and preventing cyber threats. AI will be a great tool for organizations to protect against cyber threats but also for threat actors to provide more sophisticated attacks.
The growing number of ransomware attacks on businesses and institutions shows that a stronger and smarter detection and prevention system is needed to reduce the number of successful attacks. Ransomware is the dominant cyber threat to organizations due to how lucrative it is for the attacker. The problem statement addresses the role artificial intelligence can play in mitigating ransomware and malware attacks. The research will look into AI as it applies to network security including intrusion detection and prevention, threat analysis, network security posture and incident response. Traditional methods of threat detection and prevention will be compared to AI methods to evaluate effectiveness.
Businesses are experiencing a cyber security talent shortage, leaving critical roles vacant and current IT staff overloaded with network responsibilities (Rashotte, 2023). ComputerWeekly reported that 70% of digital leaders globally feel they can’t keep up with tech trends because of the lack of skilled workers. On average, digital leaders are losing about 11% of their team every year, many because staff are looking for higher salaries (McDonald, 2022). Cybercrime is becoming more sophisticated and widespread with more and more businesses experiencing a cyber security breach. According to Rashottle (2023) in the past year, 84% of organizations surveyed said they fell victim to a cyberattack. Nearly a third of enterprises confirmed they suffered five or more breaches representing a 53% increase over the previous year (Rashotte, 2023). With this alarming information businesses need an improved security solution that can adapt and learn as the threat landscape changes. The ability of AI to learn without human intervention and analyze vast amounts of data can help IT departments with limited resources.
This research aims to provide an answer to whether AI can defend against the growing number of security threats alone or will cyber security professionals and traditional methods still be needed. AI can process large amounts of data very quickly and predict threats and identify anomalies much quicker and with higher accuracy than a trained IT professional (Brown, 2019). AI has good recognition effect but may not judge the situation accurately. Human beings can give a quick judgement of network changes as they are more flexible. AI may not judge the same situation the same way or accurately (Zhang et al., 2021). A traditional security system is the installation of a firewall to prevent unauthorized access. An intrusion detection and prevention system is based on passive measures that use statistical analysis and look for known signatures of threats (Demertzis & Iliadis, 2015). AI systems must be trained with data sets and qualified IT professionals which can be a significant financial and resource investment. Without large volumes of data, AI systems may not provide accurate results or even provide false positives (Belani, n.d.). At this point in time human intelligence is superior to AI because humans have the ability to understand, reason, perceive, achieve goals, generate language, create art, and summarize information, thus making human intelligence ideal to train AI (Iyer and Umadevi, 2023).
Rapid response to a cyber attack is crucial to prevent a network breach and loss of data. AI machine learning techniques may use information to detect breaches after they occur to provide alerts to possible impending breaches before they occur by detecting attempts to scan a network or deliver malware payloads (Wolf, 2021). AI tools may be used to aid in isolating threats before they can damage systems or used to collect forensic data to aid incident response and recovery (Wolf, 2021).
3.1 Aim and objectives.
The aim of this project is to examine the use of AI in network security in relation to traditional security tools including the limitations of an AI based security approach.
To achieve the above aim, the following objectives would be addressed during the project:
AI based threat detection and prevention methods in comparison to traditional signature-based detection methods.
How AI can use behavior analysis to learn normal network traffic and differentiate between potential threatening traffic.
Vulnerability management through the use of AI to automatically update and patch discovered vulnerabilities.
AI as a replacement for traditional security methods and security personnel or a additional tool.
3.2 Research Questions
What role will AI play in the detection and prevention of malware and how can it be used effectively against cyber threats to secure enterprise networks?
Will AI replace IT professionals, or will it be another tool technicians and engineers will use to protect network security?
3.3 Methodology
Academic platforms such as Google Scholar and SpringerLink will be used to search for publications related to artificial intelligence, network security, cyber security, and machine learning. Other platforms used for works published in the related area of study are Science Direct, ACM Digital Library, IEEE Xplore, and ResearchGate. Searches related to job security and jobs eliminated by AI will be included to discuss the impact AI will have on the IT professional.
REFERENCES
Anandita Iyer, A., Umadevi, K.S. (2023). Role of AI and Its Impact on the Development of Cyber Security Applications. In: Sarveshwaran, V., Chen, J.IZ., Pelusi, D. (eds) Artificial Intelligence and Cyber Security in Industry 4.0. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-99-2115-7_2
Brown, T. (2019, March 7). How AI is changing the cybersecurity landscape. ITChronicles. https://itchronicles.com/security/how-ai-is-changing-the-cybersecurity-landscape/
Demertzis, K., & Iliadis, L. (2015). A bio-inspired hybrid artificial intelligence framework for cyber security. SpringerLink. https://doi.org/10.1007/978-3-319-18275-9_7
McDonald, C. (2022, November 3). Almost 70% of businesses held back by talent shortage. ComputerWeekly.com. https://www.computerweekly.com/news/252526731/Almost-70-of-businesses-held-back-by-talent-shortage
Rashotte, R. (2023, March 23). Fortinet 2023 skills gap report: How organizations can fill the talent shortage. CSO Online. https://www.csoonline.com/article/574881/fortinet-2023-skills-gap-report-how-organizations-can-fill-the-talent-shortage.html
Wolf, C. (2021, April 1). Breaking down the pros and cons of AI in cybersecurity. ASIS. https://www.asisonline.org/security-management-magazine/monthly-issues/security-technology/archive/2021/april/breaking-down-the-pros-and-cons-of-ai-in-cybersecurity/
Zhang, Z., Ning, H., Shi, F., Farha, F., Xu, Y., Xu, J., Zhang, F., & Choo, K. R. (2021). Artificial intelligence in cyber security: Research advances, challenges, and opportunities. Artificial Intelligence Review, 55(2), 1029-1053. https://doi.org/10.1007/s10462-021-09976-0
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