plz see the attached files

plz see the attached files
Qualitative Data Collection Instrument Topic is “Enhancing Cyber Security In Healthcare -With The Help Of Machine Learning”. Research Questions: How can we control the access to sensitive healthcare information and systems? How to provide data security for affected healthcare data breaches? How to enhance the cybersecurity in healthcare to overcome the cyber attacks ? Overview: Using the topic and research question you developed in week 1, you will design a qualitative instrument that could potentially answer your topic/research question if it were to be applied to a qualitative study. Keep in mind, this may take some stretching if you wrote your question leaning quantitatively. The purpose here is not to box you in but to ensure that you have a solid understanding of both methodologies. Directions: You will develop a word document to include: View examples to make sure you understand the expectations of this assignment. Qualitative Instrument Samples.pdf Qualitative Instrument Samples.pdf – Alternative Formats Your research question in the form of a qualitative question (if it was not already). An instrument or protocol (interview, ethnography, focus group protocol, etc) that could be used to answer the qualitative version of your research question. A one paragraph description/justification of how your chosen instrument/protocol is the best choice for answering the qualitative version of your research question.
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I have chosen the topic for mock dissertation is “Enhancing Cyber Security In Healthcare -With The Help Of Machine Learning”. As, machine learning is expected to improve healthcare cybersecurity over the by automating network defenses and learning hacker behavior. Cyber threats are a constant concern for enterprises and are expected to cause over one trillion dollars in damages by 2018, the report predicted. To better defend against these attacks, cybersecurity vendors are considering machine learning to provide more dynamic and intuitive defenses. (O’Dowd, 2017)Machine learning has proved useful in healthcare analytics, with providers and vendors looking to apply the technology to security solutions to protect clinical health data store on-premises and in the cloud. Applying machine learning to different health IT infrastructure solutions is going to transform healthcare. Machine learning can automate processes at a much faster rate than is currently possible with staff monitoring every aspect of the network. Health records have become a valuable commodity among hackers because electronic health records (EHRs) offer identity information more comprehensive than almost any other type of record. Beyond bank account numbers, credit card information, and Social Security numbers, these records include family members’ names and ages, residential history, and every medical visit and diagnosis. When this information lands in the hands of nefarious persons the results can range from fraud to identity theft to extortion. As with every industry, healthcare increasingly relies on information technology (IT) and digital connectivity to work effectively and innovatively. This dependence introduces other security dangers and potential interference.(Dillon, 2021) In healthcare, cyberattacks can cause disruptions that prevent patients from getting critical care and quite literally cost lives. In 2019 hackers hit 90% of hospitals with email-based cyberattacks, attempting to gain unauthorized access to private data. This resulted in downtime for 72% of the targeted organizations. Additionally, mobile devices and cloud services used by healthcare institutions and healthcare professionals are also under attack. Almost 38% of healthcare organizations reported breaches to their devices in the previous year, according to a 2020 Verizon report. Research Questions: How can we control the access to sensitive healthcare information and systems? How to provide data security for affected healthcare data breaches? How to enhance the cybersecurity in healthcare to overcome the cyber attacks ? The recent growth of digital health initiatives like telehealth doctor visits during the pandemic has been a major contributor to increasing breaches. As more healthcare functions continue to move online, it’s essential to ensure these processes are protected. I believes that the healthcare industry needs government funding to strengthen their IT resources. But there are also a number of best practices healthcare organizations can implement now that will help them more effectively secure valuable healthcare data, such as educating healthcare staff, restricting access to data and applications, implementing data usage controls, and more. Effective management of healthcare data access controls addresses these questions to keep information and systems safe. Access controls allow healthcare organizations to manage their data and decide who has access to it. First, access controls help authenticate a user’s identity, guaranteeing that users are who they say they are. Next, these controls authorize access to secure information, determining whether a user has permission to take a certain action or view a specific item. Together, authentication and authorization keep data secure. The best way to keep data secure is to make it available only on a need-to-know basis. Access controls allow for this. Healthcare organizations must determine what information is relevant to whom and set access controls accordingly. After all, the data relevant to a billing specialist may not be relevant to a physician, and vice versa. Accounting for these differences and setting controls accordingly allows healthcare organizations to limit unnecessary risks. In many real-world healthcare scenarios more than one party may need to access the data such as i) the patient being monitored, ii) his/her doctor, and iii) in an emergency, other health care personnel. In these cases, conventional encryption schemes cannot handle the sharing of the secret key among multiple parties. Encrypting the data using each party’s public key is not a solution either since it creates duplicates of the data, which must be managed separately.(Kocabas et.al, 2016) Attribute based encryption (ABE) allows secure sharing of data among multiple parties. ABE is a public-key crypto-system that provides fine-grained access control similar to Role Based Access Control. Only the users whose credentials/attributes satisfy the rules determined by the access policy can retrieve the data. References: O’Dowd, E. (2017). How Machine Learning Can Improve Healthcare Cybersecurity. Dillon, L. (2021). Top 5 Healthcare Problems & Solutions In 2021. Kocabas, O., Soyata, T., & Aktas, M. K. (2016). Emerging security mechanisms for medical cyber physical systems. IEEE/ACM transactions on computational biology and bioinformatics, 13(3), 401-416.