TOPIC

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Data security

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Overview

Due to an increase in the rate at which people depend on digital life and the emergence of the internet, there has been a rise in various security incidents, such as malware attacks. There have been cases of the data breach that have been recorded severally, and there have been increased intrusions and unauthorized data access to the information and data from an organization (N. Saxena & E. Hayes et al. 2020). These have grown exponentially in that there have been many cases of the data being attacked for no reason. Prevention of these occurrences is what we call data security. It protects data from all possible ways to be attacked (N. Saxena & E. Hayes et al. 2020). The enforcement of data security requires many technologies that should be used to ensure there is physical security in the data. We should have different detection techniques used to make sure there are data security threats (N. Saxena & E. Hayes et al. 2020).

Background and problem statement

In the past years, securing data was not a big problem since fewer devices were used to steal data. In recent years there has been an emergence of many devices like USBs, phones, tablets and other devices that thefts or attackers frequently use to access the data or the information they need from a computer of an organization or a company (N. Saxena & E. Hayes et al. 2020). This makes it easy and a threat to data security; hence there has been an increase in threats on data security. These problems have shown a rise in the companies that primarily use the technologies like computers to store their company data and information (N. Saxena & E. Hayes et al. 2020).

Purpose of study

This study aims to get to know which are various data science techniques can be used or are geared toward the enhancement of data security, which data science approach is the most beneficial in the implementation of the algorithm that is detected to the anomaly detection (N. Saxena & E. Hayes et al. 2020). The anomaly detection techniques include; the k-means, the k-medoids, EM clustering, outlier detection algorithms, the classification tree, the fuzzy logic, naïve Bayes networks, genetic algorithm, etc. are well known. This study will give us ways to use these detection methods, and it will provide a review of the effectiveness of these detection methods (N. Saxena & E. Hayes et al. 2020).

Significance of study

The significance of these studies is to know and appreciate the different forms of techniques used to detect data insecurity. To understand the importance of an organization. To see and analyze the various methods used in the detection of the security data. To see and explore the essential techniques that can be used to detect the security in data (I. H. Sarker & A.S. Kayes, et al., 2020).

Research questions

What is data security? What is detections security? What are the various techniques in the detection of data security? How do an organization ensure that they have enough security in their data, or rather how the organization know that they have achieved data security on their data? Which did most scientists perceive anomaly detection techniques as the most efficient to use? (I. H. Sarker & A.S. Kayes, et al., 2020). What are the classifications of the techniques used in data security detection? How is data security threatened in an organization?

Limitations of study

Some of the organizations might not share how and what techniques they use to detect the difference. They might be afraid to share how they secure their data in the organization. The literature review of this topic is less as compared to other studies. The information available for this research is limited and small in quantity. This research is expensive since it needs a practical aspect of the detection methods, which might be costly (I. H. Sarker & A.S. Kayes et al., 2020).

Definitions

Data security, k-means, k-medoids, EM clustering, outlier detection, physical security, fuzzy logic, perceived efficacy, data science, anomaly detection, centroid clusters, disjoint clusters, algorithms, genetic algorithms.

Summary

Data security is an essential thing in every organization. It is a necessary thing if an organization. It the obligation of each organization to ensure that their data is secured. This will only be checked through the techniques discussed in this study and research paper (I. H. Sarker & A.S. Kayes, et al., 2020). Data security helps keep the confidential information of every employee in an organization with some privacy. Physical security of data is typically neglected, but it is also an essential aspect of data security. Therefore physical security and cybersecurity should be employed in each organization (I. H. Sarker & A.S. Kayes et al., 2020).

References

Saxena, N., Hayes, E., Bertino, E., Ojo, P., Choo, K. K. R., & Burnap, P. (2020). Impact and key challenges of insider threats on organizations and critical businesses. Electronics, 9(9), 1460. https://www.mdpi.com/2079-9292/9/9/1460

Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from a machine learning perspective. Journal of Big Data, 7(1), 1-29. https://link.springer.com/article/10.1186/s40537-020-00318-5