Mock Dissertation Chapter One Introduction Overvie

Mock Dissertation Chapter One Introduction

Overview

In today’stoday’s world, organizations require high levels of security to protect sensitive information. While companies do their best to protect their information, criminals continue to attempt to access this data?the number of breaches increases almost weekly. The National Cyber Security Alliance stated that ” a successful cyberattack costs the US economy $8.6 billion annually,” and many businesses lose more than $100 billion a year due to data breaches. It is no longer a surprise when an individual loses their data. Therefore, people can no longer trust that their information will not be compromised. As a result, companies have to take steps to prevent their data from being compromised constantly. As data breaches have become so common, many people use security programs and services that require them to provide personal information, such as passwords. Unfortunately, these systems are often vulnerable to attack. Once they have the correct information, they may be able to access a company’s sensitive information (National Cyber Security Alliance, 2020). Hackers use viruses, “trojans,” social engineering attacks, and other methods to enter an organization’s computer systems.

Once inside, hackers may steal data or corrupt it, exposing a company to loss of reputation and legal action. In the end, a company must invest resources in detecting and recovering the lost data (National Cyber Security Alliance, 2020).

The research uses an annual survey by the Center for Internet Security (CIS) that stated that “hackers now attack about a billion computers per month,” and the number of breaches has grown to 200,000 a year. According to the NCSA number of breaches occurring each year exceeds 6,800. As the number of attacks has increased, businesses are now relying on data science to increase their security and protect their company. However, data science does not provide a 100% guarantee complete protection. For example, if an employee provides information, but he or she does not use the same password for every website. In other words, a

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data scientist cannot guarantee that the data will be 100% protected. However, multiple security measures are adequate, including detection and prevention before an attack. Furthermore, a security expert might be able to identify a potential breach and act accordingly. By having multiple systems in place to protect the information, a company can limit the damage if a breach does occur.

Background and Problem Statement

Several software and services can monitor personal and company information for any suspected unauthorized activity. However, the data collected is often stored in proprietary databases that use a “one size fits all” approach to information security. It makes it challenging to identify the breach and causes a company to lose confidence that the data is secure. Data science may provide valuable insights and intelligence for security analysts and employees (Zhang et al., 2018). Unfortunately, there are several challenges associated with incorporating data science into security. A key challenge is that data science data is not in the same format or location as the raw data collected by security systems. Data science cannot currently easily be combined with security systems.

Consequently, there is a need to integrate information collected from multiple systems. For example, a security system might collect the time a computer connects to the network and the amount of time. In addition, a user might manually enter information such as credit card numbers or social security numbers in logbooks (Zhang et al., 2018). Moreover, to effectively analyze data from these systems, they have to be combined and stored in a standard format and location (Zhang et al., 2018). Data science techniques developed to process large amounts of data from various sources, such as online shopping transactions, social media, search engine queries, and so on, cannot be used directly. One of the examples of a data science technique is machine learning. Machine learning is a data-driven methodology used to build a model from a data set and predict an outcome. While many companies have a large amount of data, the data

is in a proprietary database format. The model is built based on this proprietary format of data, which is often difficult to analyze by security analysts. Thus, a problem exists in combining the data from various systems into a common location for security analysts to use (Zhang et al., 2018).

Purpose Of The Study

This study aims to identify techniques and methods that can be applied in data security and enhance the ability of data science to detect security breaches. By enhancing data science with data security, companies can gain a significant advantage to protect their sensitive data. Furthermore, data science can provide data to the data security system and ensure it can detect breaches as they occur (Farooq et al., 2022). It enables the security systems to prevent breaches and detect them as soon as possible. Once the breach is detected, a data scientist can analyze the data to identify what happened and who may have accessed the data. It allows a security system to find the hackers responsible for the breach. Moreover, if an employee suspects a security breach has occurred, he or she can use the results from the data scientist to determine if a breach has taken place. It is helpful because a person may have entered personal information into a website or on a computer using a unique password that might have caused the data to be detected by a security system (Farooq et al., 2022).

Significance of the study

This research has potentially significant implications for security analysts and personnel within an organization. If it is determined that information is stolen, data scientists can provide insights that can prevent a breach from occurring or enable the security system to quickly detect a breach (Farooq et al., 2022). Once the breach is detected, the data scientist can analyze the data and determine who may have accessed the information and where the breach occurred.

Thus, if a breach occurs and it is determined that a hacker has accessed data, then the data can be removed and prevent the individual from reaccessing the information. For example,

companies may have a database in a secure location on a network server where each entry is a unique username and password pair. If a hacker gains access to this database, he or she will not be able to gain access again because the system has already identified the hacker. The system can store all of the usernames and passwords, including what the person used to access the information. It gives the security system a chance to identify and track the person. In this case, data science can improve data security by analyzing the data and determining what happened after a security breach occurs (Farooq et al., 2022).

Research Questions

The following research questions will be addressed in this study. RQ 1: How can data science techniques be combined with security systems to detect security breaches effectively? (Farooq et al., 2022). RQ 2: What data can be extracted from multiple sources and stored in a standard format to detect security breaches using machine learning techniques? RQ 4: How can the machine learning techniques be applied to identify a hacker responsible for a security breach? RQ 3: How can the data from the machine learning system be fed to the data security system to ensure it can detect security breaches as they occur? (Farooq et al., 2022).

Limitations of the Study

One of the limitations is that the sample size for this study is limited to three organizations that were willing to participate in the study. Thus, the results cannot be generalized to other industries or organizations. Another limitation is that this study did not address ethical concerns such as data privacy or whether the data should be used in the first place (Farooq et al., 2022). For example, companies may provide information that can be used to identify their customers. Even though companies may be collecting this information to provide better service, the information may be provided to government agencies and other entities. Thus, it would be hard to tell if the data was given to government agencies for use against their customers. Companies also have a moral responsibility to protect information

provided to them. Additionally, companies are responsible for ensuring that information is not inappropriately used and once organizations gain access to the data, the data may be used in various ways to find and detect the threats (Farooq et al., 2022).

Assumptions

The primary assumption of this research is that the sample size is limited to three organizations that were willing to participate in the study. However, testing on these three samples can provide insight into how data science techniques can be effectively combined with data security (Patil et al., 2020). There is also an assumption that the information for machine learning can be easily acquired from data scientists who have access to the data. Because data security has become such a concern, many data scientists have access to and manipulate sensitive data that they should not. Thus, it is difficult to determine if the information from the data security systems is reliable. Therefore, if this information is not reliable, it may not be helpful for the data scientist. Furthermore, the machine learning technique will be trained and tested for accuracy, so there should be no significant difference between the results (Patil et al., 2020).

Definition

Data Science: Data science is defined as “the statistical or mathematical sciences that analyze and make sense of data from various sources (Patil et al., 2020).

Machine learning: Machine learning is defined as “an approach to artificial intelligence that gives computers the ability to learn without being explicitly programmed.” In other words, machine learning is an automated computer-based process that allows computers to “learn” from example data (Patil et al., 2020).

Data security: Data security is defined as ” a system for protecting data from unauthorized access or alteration” and is typically used in conjunction with a data protection and recovery system (DPRS) (Patil et al., 2020).

Data theft: Data theft involves “inferring and obtaining sensitive information such as confidential business information, medical records, or legal documents without permission, usually for a profit.

Data Breaches: A data breach is ” a security incident in which data is illegally acquired, used, or disclosed” and includes data theft and security systems that were improperly configured or improperly used (Cremer & Strbac, 2021).

Detection: Detecting a breach is defined as “identifying a possible security incident.” It can be used in this study to identify hackers responsible for a breach and where the breach occurred.

Supervised learning: Supervised learning is defined as “learning a model by looking at patterns in a training set.

Unsupervised learning: Unsupervised learning is defined as “learning a model by analyzing patterns in unlabeled data (Cremer & Strbac, 2021).

Summary

This chapter’s principal components were overview, background, purpose statement, and limitations. In addition, the process of using data science to enhance security systems was explained. Furthermore, the primary data science techniques and methods that can be used to extract information from data were presented (Cremer & Strbac, 2021). These three categories are the most commonly used for performing data analysis. In addition, the fundamental machine learning algorithm for these techniques was explained. Moreover, the fundamental machine learning techniques used in data analysis were explained. In addition, the process of applying machine learning techniques for enhanced data security was discussed. Moreover, the two most commonly used machine learning techniques for performing data analysis were explained. The SVM approach is called a supervised learning technique and is the most common technique used for detecting a breach in this study. The SVM approach is a form of

machine learning used to perform data analysis. The ANN approach is called an unsupervised learning approach and is the second most commonly used technique to detect a breach. This approach is used to find similarities or patterns in data sets collected. The study results are to understand how machine learning can be combined with security systems to detect security breaches (Cremer & Strbac, 2021).

References

Cremer, J. L., & Strbac, G. (2021). A machine-learning-based probabilistic perspective on a dynamic security assessment. International Journal of Electric(al Power & Energy Systems, 128, 106571.

Farooq, U., Tariq, N., Asim, M., Baker, T., & Al-Shamma’aAl-Shamma’a, A. (2022). Machine learning and the Internet of Things security: Solutions and open challenges. Journal of Parallel and Distributed Computing, 162, 89-104.

National Cyber Security Alliance., (2020). Data Privacy Day 2020 Report. https://staysafeonline.org/resource/dpd20-report/

Patil, B. P., Kharade, K. G., & Kamat, R. K. (2020). Investigation on Data Security Threats & Solutions. International Journal of Innovative Science and Research Technology, 5(1), 79-83.

Zhang, J., Yanchao, Z., Bing, C., Feng, H. U., & Kun, Z. H. U. (2018). Survey on data security and privacy-preserving for the research of edge computing. Journal on Communications, 39(3), 1.

R.1 Abstract of Qualitative Research Article

Overview:?

The term ?abstract? is a homophone which can mean one of two scholarly writing activities. One, is the abstract that you will write to introduce your dissertation. The other meaning is a shortened writing assignment whereby you write a condensed summary of an academic journal.? For this assignment, we will focus on writing a scholarly abstract of a qualitative journal. More information about writing an abstract can be found via the web resource ?Writing Scholarly Abstracts.?

Directions:?

View the rubric and examples to make sure you understand the expectations of this assignment. Create a 2 and half page single-spaced Analysis of Research abstract published qualitative scholarly article related to your mock dissertation topic/research question.

Brevity and being concise are important as this analysis is intended to be a brief summation of the research.

Each abstract must therefore consist of the following in this order:

1. Bibliographic Citation?? use the correctly formatted APA style citation for the work as the title of your abstract, displaying the full citation in bold font.

2. Author Qualifications?? name and qualification of each author conducting the research

3. Research Concern?? one paragraph summary of the reason for the overall research topic

4. Research Purpose Statement AND Research Questions or Hypotheses?? specific focus of the research

5. Precedent Literature ??key literature used in proposing the needed research (not the full bibliography or reference list)

6. Research Methodology ??description of the population, sample, and data gathering techniques used in the research

7. Instrumentation ??description of the tools used to gather data (surveys, tests, interviews, etc.)

8. Findings ??summation of what the research discovered and the types of analysis that were used to describe the findings (tables, figures, and statistical measures)

R.2 Abstract of a Quantitative Research Article

Overview:?

The term ?abstract? is a homophone which can mean one of two scholarly writing activities. One, is the abstract that you will write to introduce your dissertation. The other meaning is a shortened writing assignment whereby you write a condensed summary of an academic journal.? For this week, we will focus on writing a scholarly abstract of a quantitative journal. More information about writing an abstract can be found via the web resource ?Writing Scholarly Abstracts.?

Directions:?

View the rubric and examples to make sure you understand the expectations of this assignment.? Create a 2 and half page single-spaced Analysis of Research abstract published quantitative scholarly article related to your mock dissertation topic/research question. Additionally, this assignment functions just like assignment R.1 only it reviews a quantitative article instead of a qualitative one.