Instructions: Machine Learning Algorithms Cyber Threats 1-Use 1 reference only 1 time for one paragraph

Subject:EconomicsPrice:19.89 Bought3

Share With

Instructions: Machine Learning Algorithms Cyber Threats

1-Use 1 reference only 1 time for one paragraph. Don’t use multiple authors in 1 paragraph.

2-For every reference use the keywords like the author findings, quantitative method, Approach, Gaps, Problem, theoretical basis, backup evidence, future scope.

3-Use Grammarly

4- Theoretical Orientation for the Study is very important. It would help if you talked about this.  Where it comes from, how it was developed, who has used it since its foundation. 

5- Talk about the variables of your theoretical model and its constructs.
Next
Start your literature review. This is all the theoretical orientation in action. How has it been used, when has it be used, and some of the results that have been used. Review the theoretical orientation and review those studies. 

6 - Based on your theoretical orientation, you should be picking up a literature review of those things towards the body of knowledge.

7- Based on the work use suitable Headings and Sub-Headings(APA Format)

Example of what I expect in the literature review:

1- The author expressed his view and proposed a new model in his paper. According to the author...no you need to explain the model and its function. 

2- Finally, the result of the model and comparison of that model with the already existing model...
3- Last, you need to mention the weak point (the gap in the model). 

Look for the week points. Is that weak point what you are going to be working on...

 

Please use provide 22 References only

Arif, M. (2021). A Systematic Review of Machine Learning Algorithms in Cyberbullying

Detection: Future Directions and Challenges. Journal of Information Security and

Cybercrimes Research, 4(1), 01-26. https://doi.org/10.26735/gbtv9013

Chaudhary, K., Alam, M., Al-Rakhami, M. S., & Gumaei, A. (2021). Machine learning-based

mathematical modelling for prediction of social media consumer behavior using big data

analytics. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00466-2

Dini, P., & Saponara, S. (2021). Analysis, Design, and Comparison of Machine-Learning

Techniques for Networking Intrusion Detection. Designs, 5(1), 9.

https://doi.org/10.3390/designs5010009

Kothari, S. (2021). USE OF MACHINE LEARNING ALGORITHMS IN CYBER SECURITY. INFORMATION TECHNOLOGY in INDUSTRY, 9(2), 1214–1219. https://doi.org/10.17762/itii.v9i2.475

Larriva-Novo, X., Villagrá, V. A., Vega-Barbas, M., Rivera, D., & Sanz Rodrigo, M. (2021). An

IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets. Sensors (Basel, Switzerland), 21(2), 656–. https://doi.org/10.3390/s21020656

Leevy, J. L., Hancock, J., Zuech, R., & Khoshgoftaar, T. M. (2021). Detecting cybersecurity

attacks across different network features and learners. Journal of Big Data, 8(1), 1–29. https://doi.org/10.1186/s40537-021-00426-w

Liu, L., Wang, P., Lin, J., & Liu, L. (2021). Intrusion Detection of Imbalanced Network Traffic

Based on Machine Learning and Deep Learning. IEEE Access, 9, 7550–7563. https://doi.org/10.1109/ACCESS.2020.3048198

Malhotra, P., Singh, Y., Anand, P., Bangotra, D. K., Singh, P. K., & Hong, W.-C. (2021).

Internet of Things: Evolution, Concerns and Security Challenges. Sensors (Basel, Switzerland), 21(5), 1809–. https://doi.org/10.3390/s21051809

Mathew, A. (2021). Machine Learning in Cyber-Security Threats. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3769194

Moualla, S., Khorzom, K., & Jafar, A. (2021). Improving the Performance of Machine Learning-

Based Network Intrusion Detection Systems on the UNSW-NB15 Dataset. Computational Intelligence and Neuroscience, 2021, 1–13. https://doi.org/10.1155/2021/5557577

Prajapati, A., & Gupta, S. (2021). A Survey : Data Mining and Machine Learning Methods for

Cyber Security. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 24–34. https://doi.org/10.32628/cseit217212

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

Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2021). Adversarial Machine Learning

Attacks and Defense Methods in the Cyber Security Domain. ACM Computing Surveys, 54(5), 1–36. https://doi.org/10.1145/3453158

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

Sharma, N., Sharma, R., & Jindal, N. (2021). Machine Learning and Deep Learning Applications-A Vision. Global Transitions Proceedings. https://doi.org/10.1016/j.gltp.2021.01.004

Siraskar, R. (2021). Reinforcement learning for control of valves. Machine Learning with Applications, 100030. https://doi.org/10.1016/j.mlwa.2021.100030

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

Ulven, J. B., & Wangen, G. (2021). A Systematic Review of Cybersecurity Risks in Higher

Education. Future Internet, 13(2), 39–. https://doi.org/10.3390/fi13020039

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

Warikoo, A. (2021). The Triangle Model for Cyber Threat Attribution. Journal of Cyber Security Technology, 1–18. https://doi.org/10.1080/23742917.2021.1895532

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

Zoppi, T., Ceccarelli, A., & Bondavalli, A. (2021). Unsupervised Algorithms to Detect Zero-Day

Attacks: Strategy and Application. IEEE Access, 9, 90603–90615.

https://doi.org/10.1109/ACCESS.2021.3090957 

Machine Learning Algorithms Cyber Threats Outline

Introduction

• Definition of Cyber Threats.
• Machine to machine interactions and loopholes in machine learning algorithms.
• Exfiltration attacks and counteraction od deep learning algorithm.

Literature review

• Intrusion detections in Internet of Things and imbalanced network traffic.
• Vulnerabilities in smart system networks.
• Using machine learning algorithms in social media platforms.
• Types of cyber-attacks.
• Nonlinear valves in machine learning.
• Security governance of information.
• Cybersecurity in higher education
• Cyber Supply Chain security.
• Machine learning as an artificial intelligence.
• Unsupervised anomaly detection (zero-day attacks).