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Database Manager Job
Database manager job can be described as a job that involves a qualified person's role in
overseeing an organization's information storage and retrieval system. Database managers are
times referred to as database administrators. The primary role played by database managers in
any sector is to ensure the ...
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San Jose State University Machine Learning Algorithms Cyber Threats Research Paper
Instructions: Machine Learning Algorithms Cyber Threats
1-Use 1 reference only 1 time for one paragraph. Don’t use multi ...
San Jose State University Machine Learning Algorithms Cyber Threats Research Paper
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).
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Write a 300 word essay address the question below
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Write a 300 word essay address the question below
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Explain what you learned in your own words, and do your best to synthesize the authors' analyses into your own original analysis;
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Please include the questions with your answers. Answer in complete sentences where applicable.1.Explain what a modificatio ...
Grantham University Girls Cross Country Team Modification Problems Questions
Please include the questions with your answers. Answer in complete sentences where applicable.1.Explain what a modification problem is. What are the three possible types of modification problems? Use the following spreadsheet segment to answer the next three questions:Girls’ Cross-Country Team2.Describe 2 problems that the Girls’ Cross-Country Team spreadsheet could present. Be specific, using information in the spreadsheet in your descriptions.3.Break the spreadsheet into two tables, describing what information is contained in each one.4.Describe how the two tables will solve the two problems you described in #2.5.What does SQL stand for? What is its purpose?6.Describe the use of an index.7.What are the ten functions of a DBMS?8.Define the term “referential integrity constraint” and give an example.9.What is the difference between a database, a DBMS, and a database application?10.What is a NoSQL database? What are Web 2.0 applications?
San Jose State University Machine Learning Algorithms Cyber Threats Research Paper
Instructions: Machine Learning Algorithms Cyber Threats
1-Use 1 reference only 1 time for one paragraph. Don’t use multi ...
San Jose State University Machine Learning Algorithms Cyber Threats Research Paper
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).
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