SJSU Machine Learning Algorithms Paper

1. Introduction

Give the introduction 

1.machine learning algorithms can detect the cyber threats.——1 paragraph

Huc, A., & Trcek, D. (2021). Anomaly Detection in IoT Networks: From Architectures to 

Machine Learning Transparency. IEEE Access, 9, 60607–60616. https://doi.org/10.1109/ACCESS.2021.3073785

2. application of machine learning techniques for cyber threats( only Logistic Regression)—-1 paragraph

Kumari, K., Mrunalini, M. Detecting Denial of Service attacks using machine learning algorithms. J Big Data 9, 56 (2022). https://doi.org/10.1186/s40537-022-00616-0

2. Limitations of the Study

Note: Modify the paragraph

This study has some limitations that may have an impact on the results. Participants from various organizations provided responses. As a result, the findings may not apply to the entire global population. Furthermore, the study used a quantitative research method with a validated survey instrument to investigate the relationship between the use of machine learning techniques and cyber threats, as well as the machine learning algorithms related to cyber threat detection. The application of a survey proposes inherent limitations, such as participants’ perception and responsibility in answering survey items and demographics. The survey questionnaire cannot be determined because the only data gathered is based on the questions asked, with no opportunity for follow-up questions or any way to recognize nonverbal performance.

3. Implications for Future Study— 2 paragraphs

Keywords

Cyber threats, machine learning algorithms, machine learning techniques, Artificial intelligence (future study)

Reference 1——– one paragraph

Halbouni, Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). 

Machine Learning and Deep Learning Approaches for CyberSecurity: A Review. IEEE 

Access, 10, 19572–19585. https://doi.org/10.1109/ACCESS.2022.3151248

Reference 2 -one paragraph

Venkatesh. (2021). Adoption and use of AI tools: a research agenda grounded in UTAUT. 

Annals of Operations Research, 308(1-2), 641–652. https://doi.org/10.1007/s10479-020-03918-9 

4. Summary

1 paragraph