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San Jose State University Machine Learning Algorithms for Cyber Threats Paper

Instructions:

1

Use suitable content names Like Introduction, Theoretical framework, existing work, future scope, research findings, etc

2

Everything should be cited from provided references only.1.

3

Be sure you know that you must keep your literature in line with your theoretical framework. When conducting literature reviews. You must review one at a time. Then, document the next review, one author at a time.

3.

Use Grammarly and APA format only

References

1

Alkahtani, H., & Aldhyani, T. H. H. (2021). Intrusion Detection System to Advance Internet of

Things Infrastructure-Based Deep Learning Algorithms. Complexity (New York, N.Y.), 

2021. https://doi.org/10.1155/2021/5579851

2

Al-Mhiqani, M. N., Ahmad, R., Zainal Abidin, Z., Yassin, W., Hassan, A., Abdulkareem, K. H., Ali, N. S., & Yunos, Z. (2020). A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations. Applied Sciences, 10(15), 5208–. https://doi.org/10.3390/app10155208

3

Aravindan, C., Frederick, T., Hemamalini, V., & Cathirine, M. V. J. (2020). An Extensive Research on Cyber Threats using Learning Algorithm. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 1–8. https://doi.org/10.1109/ic-ETITE47903.2020.337 

4

Bilen, A., & Özer, A. B. (2021). Cyber-attack method and perpetrator prediction using machine learning algorithms. PeerJ. Computer Science, 7, e475–e475. https://doi.org/10.7717/peerj-cs.475

5

Laghrissi, F., Douzi, S., Douzi, K., & Hssina, B. (2021). Intrusion detection systems using long 

short-term memory (LSTM). Journal of Big Data, 8(1), 1–16. https://doi.org/10.1186/s40537-021-00448-4 

6

Wani, A., S, R., & Khaliq, R. (2021). SDN?based intrusion detection system for IoT using deep 

learning classifier (IDSIoT?SDL). CAAI Transactions on Intelligence Technology. 

https://doi.org/10.1049/cit2.12003 

7

Hammad, M., Hewahi, N., & Elmedany, W. (2021). T?SNERF: A novel high accuracy machine 

learning approach for Intrusion Detection Systems. IET Information Security, 15(2), 

178–190. https://doi.org/10.1049/ise2.12020 

8

Rincy N, T., & Gupta, R. (2021). Design and Development of an Efficient Network Intrusion 

Detection System Using Machine Learning Techniques. Wireless Communications and Mobile Computing, 2021, 1–35. https://doi.org/10.1155/2021/9974270 

9

Sabir, B., Ullah, F., Babar, M. A., & Gaire, R. (2021). Machine Learning for Detecting Data

Exfiltration: A Review. ACM Computing Surveys, 54(3), 1–47. 

https://doi.org/10.1145/3442181

10

Slayton, R. (2021). Governing Uncertainty or Uncertain Governance? Information Security and 

the Challenge of Cutting Ties. Science, Technology, & Human Values, 46(1), 81–111. https://doi.org/10.1177/0162243919901159

11

Yeboah-Ofori, A., Islam, S., Lee, S. W., Shamszaman, Z. U., Muhammad, K., Altaf, M., & Al-

Rakhami, M. S. (2021). Cyber Threat Predictive Analytics for Improving Cyber Supply 

Chain Security. IEEE Access, 1–1. https://doi.org/10.1109/ACCESS.2021.3087109 

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