Qualitative Research Review Paper

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University of the Cumberlands *

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DSRT - 734

Subject

Computer Science

Date

Feb 20, 2024

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docx

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5

Uploaded by patilsushant26

Qualitative Research Review Sushant Anil Patil Department of Information Technology DSRT -734 Inferential Statistics in Decision Making Dr. Doug Bennett July 29 th , 2023
Qualitative Research review for the paper “Cybersecurity data science: an overview from machine learning.” Introduction: The increasingly sophisticated nature of cyber-attacks has made cybersecurity a crucial worry in the modern digital era. Researchers and practitioners have used data science and machine learning techniques to strengthen their cybersecurity defenses. The research study regarding data science in cybersecurity (Sarkar et al,. 2020) explores the relationship between cybersecurity and data science, providing information on cutting-edge approaches and difficulties in using machine learning to defend against cyber threats. This review evaluates the paper's significant contributions, techniques, and cybersecurity field implications and offers suggestions for improving the article. Methodology and Framework: The authors extensively review the current machine learning techniques, including decision trees, support vector machines (SVM), deep learning, and ensemble techniques. They also go over how crucial feature engineering and data pretreatment are to improving the performance of these models. The research also examines the difficulties presented by imbalanced datasets, a topic frequently arising in cybersecurity, and suggests remedies. Key Findings: The study offers a thorough analysis of data science's use in cybersecurity, illuminating the many stages of the lifecycle of a cyberattack when machine learning algorithms can be used successfully.
It investigates the use of machine learning models for cyber threat detection purposes, including behavior analysis, signature-based methods, and anomaly detection. The paper explores how data science can be used to discover system and network vulnerabilities, enabling preventative security solutions. Furthermore, the authors also discuss using machine learning approaches for malware categorization and detecting advanced persistent threats (APTs). At last, the necessity of predictive analytics in cybersecurity is emphasized in the study to foresee and stop possible cyberattacks. Analysis and Critique: The paper offers a helpful and comprehensive summary of the subject. The intersection of cybersecurity and machine learning is accomplished, providing readers with a comprehensive knowledge of the developments in this interdisciplinary topic. One drawback is that it predominantly emphasizes the machine learning viewpoint, potentially ignoring other essential cybersecurity strategies like network security and cryptography. Machine Learning models are primarily designed to investigate patterns and behaviors. Cyber-security risks often depend on access level, network security, and cryptography (Ahsan et al., 2022). The paper can benefit from offering a more thorough assessment of various approaches in cybersecurity data science rather than concentrating on specific machine learning methods. Reading about the advantages and disadvantages of various strategies will assist readers in selecting the methods that are best suited to their cybersecurity requirements (Furdek et al., 2021). Conclusion: In conclusion, the paper presents a commendable effort in bridging the gap between cybersecurity challenges and data science solutions. By exploring the methodologies and
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