Phishing Essays (Examples)
Study Document
What Is Cybercrime And How To Deter It
… digitally sophisticated, and able to operate with a degree of professionalism and discipline that allows them to hide their crimes beneath legitimate-looking facades. Spearphishing is one example of a type of cybercrime used by professional criminals to lure victims into traps or sites that have the appearance … Act is another federal law that along with the Stored Communications Act makes it a criminal offense to access information with authorization. Hacking, phishing and denial of service attacks are all prosecuted under these laws. Most states have taken steps to pass cybercrime bills and the majority … Prevent Cybercrime
One of the best ways to prevent cybercrime is to educate the end user. The vast majority of cybercrimes come from phishing scams and social engineering, both of which prey on the end user. End users should know better than to click on suspicious links ……
References
References
Computer Hope. (2019). When was the first computer invented? Retrieved from https://www.computerhope.com/issues/ch000984.htm
Crane, C. (2019). 33 alarming cybercrime statistics you should know in 2019. Retrieved from https://www.thesslstore.com/blog/33-alarming-cybercrime-statistics-you-should-know/
Schjølberg, Stein. (2017). The History of Cybercrime (1976-2016). Books on Demand.
Statista. (2020). Global digital population. Retrieved from https://www.statista.com/statistics/617136/digital-population-worldwide/
Taylor, R. W., Fritsch, E. J., Liederbach, J., Saylor, M. R., & Tafoya, W. L. (2019). Cyber crime and cyber terrorism. NY, NY: Pearson.
Study Document
Clinical Informatics
… Nurses play a big role in this regard as they are end users of the technology and must safeguard passwords and beware of phishing schemes, which are used to steal access……
References
References
Cho, O. M., Kim, H., Lee, Y. W., & Cho, I. (2016). Clinical alarms in intensive care units: Perceived obstacles of alarm management and alarm fatigue in nurses. Healthcare informatics research, 22(1), 46-53.
Effken, J., Weaver, C., Cochran, K., Androwich, I., & O’Brien, A. (2016). Toward a central repository for sharing nursing informatics’ best practices. CIN: Computers, Informatics, Nursing, 34(6), 245-246.
Elsayed, W. A., Hussein, F. M., & Othman, W. N. (2017). Relation between nursing informatics competency and nurses’ attitude toward evidence-based practice among qualified nurses at Mansoura Oncology Center. International Journal of Nursing Didactics, 7(6), 26-33.
Drolet, B. C., Marwaha, J. S., Hyatt, B., Blazar, P. E., & Lifchez, S. D. (2017). Electronic communication of protected health information: privacy, security, and HIPAA compliance. The Journal of hand surgery, 42(6), 411-416.
Haupeltshofer, A., Egerer, V., & Seeling, S. (2020). Promoting health literacy: What potential does nursing informatics offer to support older adults in the use of technology? A scoping review. Health Informatics Journal, 1460458220933417.
Kharbanda, E. O., Asche, S. E., Sinaiko, A. R., Ekstrom, H. L., Nordin, J. D., Sherwood, N. E., & O’Connor, P. (2018). Clinical decision support for recognition and management of hypertension: a randomized trial. Pediatrics, 141(2).
Khezri, H., & Abdekhoda, M. (2019). Assessing nurses’ informatics competency and identifying its related factors. Journal of Research in Nursing, 24(7), 529-538.
Kleib, M., & Nagle, L. (2018). Factors associated with Canadian nurses\\\\\\\\\\\\' informatics competency. CIN: Computers, Informatics, Nursing, 36(8), 406-415.
Study Document
Ethics And Health Information
...Phishing Managing Medical Records and the Implementation of Tools and Safeguards Required within HIS
Introduction
Few practices are more important in managing health information systems than managing medical records, safeguarding patients’ medical history, and ensuring that all end users of medical information technology are approved and trained. Some of the biggest factors in security breaches are end users themselves (Rhee, Kim & Ryu, 2009). This is why training of staff on how to use equipment and the importance of protecting passwords is so important (Jackson, 2018). However, the system itself should have system protections built-in that can protect against end user mistakes—protections such as double security via multi-factor authentication (Crossler & Posey, 2017). This paper will discuss the programming language and relational databases that should be used to accommodate security needs for the HIS, the information tools and safeguards required to protect it, the security needed for electronic health records, an……
References
References
Campbell, R. J. (2004). Database Design: What HIM Professionals Need to Know.
Perspectives in Health Information Management 2004, 1:6 (August 4, 2004). Retrieved from http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_024637.hcsp?dDocName=bok1_024637
Crossler, R. E., & Posey, C. (2017). Robbing Peter to Pay Paul: Surrendering Privacy for Security's Sake in an Identity Ecosystem. Journal of The Association for Information Systems, 18(7), 487-515.
Donovan, F. (2018). Judge Gives Final OK to $115M Anthem Data Breach Settlement. Retrieved from https://healthitsecurity.com/news/judge-gives-final-ok-to-115m-anthem-data-breach-settlement
HealthIT.gov. (2018). Health Information Privacy, Security, and Your EHR. Retrieved from https://www.healthit.gov/providers-professionals/ehr-privacy-security
The IMIA Code of Ethics for Health Information Professionals. (n.d.). Retrieved from http://www.imia medinfo.org/new2/pubdocs/Ethics_Eng.pdf
Jackson, R. (2018). Pulling strings. Retrieved from https://iaonline.theiia.org/2018/Pages/Pulling-Strings.aspx
Prince, B. (2013). Programming Languages Susceptible to Specific Security Flaws: Report. Eweek, 12. Retrieved from https://www.eweek.com/security/programming-languages-susceptible-to-specific-security-flaws-report
Study Document
Critical Information Literacy
… of exploiting the human flaws to achieve a malicious objective” (Breda, Barbosa & Morais, 2017). A typical social engineering attack would be a phishing scam, but others would include hackers tricking people into providing information, which is then used against them (Kaspersky, 2020).
Protecting personal information online ……
References
References
Breda, F., Barbosa, H., Morais, T. (2017) Social engineering and cyber security. Conference Paper.
Kaspersky (2020) What is social engineering? Kaspersky Labs. Retrieved April 15, 2020 from https://usa.kaspersky.com/resource-center/definitions/social-engineering
Study Document
Privacy In Social Networks Regarding Machine Learning
… of the least informed or least prepared individuals and are typically the number one threat/risk to a system’s security. They are targeted through phishing methods and unless trained to know better can make an organization extremely vulnerable. The same goes for protecting their own data and maintaining ……
References
References
Balle, B., Gascón, A., Ohrimenko, O., Raykova, M., Schoppmmann, P., & Troncoso, C. (2019, November). PPML\\\\\\\\\\\\'19: Privacy Preserving Machine Learning. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 2717-2718). ACM.
Bilogrevic, I., Huguenin, K., Agir, B., Jadliwala, M., Gazaki, M., & Hubaux, J. P. (2016). A machine-learning based approach to privacy-aware information-sharing in mobile social networks. Pervasive and Mobile Computing, 25, 125-142.
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Seth, K. (2017, October). Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1175-1191). ACM.
Hunt, T., Song, C., Shokri, R., Shmatikov, V., & Witchel, E. (2018). Chiron: Privacy-preserving machine learning as a service. arXiv preprint arXiv:1803.05961.
Lindsey, N. (2019). New Research Study Shows That Social Media Privacy Might Not Be Possible. Retrieved from https://www.cpomagazine.com/data-privacy/new-research-study-shows-that-social-media-privacy-might-not-be-possible/
Mohassel, P., & Zhang, Y. (2017, May). Secureml: A system for scalable privacy-preserving machine learning. In 2017 IEEE Symposium on Security and Privacy (SP) (pp. 19-38). IEEE.
Mooney, S. J., & Pejaver, V. (2018). Big data in public health: terminology, machine learning, and privacy. Annual review of public health, 39, 95-112.
Oh, S. J., Benenson, R., Fritz, M., & Schiele, B. (2016, October). Faceless person recognition: Privacy implications in social media. In European Conference on Computer Vision (pp. 19-35). Springer, Cham.
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