Computer Science Homework Help

1. Introduction————–2 pages
2. Theoretical Orientation for the Study——2 Pages
3. Summary—————1 -Page 

          Mention the topic name(Machine learning for cyber threats, include the data bases, sources references found from ProQuest, ieeexplore, sciencedirect etc. (Based on provided total 15 references) in the form of sentences.

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

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

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

Ebrahimi, M., Nunamaker, J. F., & Chen, H. (2020). Semi-Supervised Cyber Threat Identification in Dark Net Markets: A Transductive and Deep Learning Approach. Journal of Management Information Systems, 37(3), 694–722. https://doi.org/10.1080/07421222.2020.1790186

Estévez-Pereira, J. J., Fernández, D., & Novoa, F. J. (2020). Network Anomaly Detection Using Machine Learning Techniques. Proceedings, 54(1), 8–. https://doi.org/10.3390/proceedings2020054008

Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: an overview from machine learning perspective. Journal of Big Data, 7(1), 1–29. https://doi.org/10.1186/s40537-020-00318-5

Zhang, S., Xie, X., & Xu, Y. (2020). A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity. IEEE Access, 8, 128250–128263. https://doi.org/10.1109/ACCESS.2020.3008433

Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2021). Network

intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32(1). https://doi.org/10.1002/ett.4150

Aloseel, A., Al-Rubaye, S., Zolotas, A., & Shaw, C. (2021). Attack-Detection Architectural

Framework Based on Anomalous Patterns of System Performance and Resource

Utilization-Part II. IEEE Access, 9, 1–1. https://doi.org/10.1109/ACCESS.2021.3088411

Bagui Sikha, & Li Kunqi. (2021). Resampling imbalanced data for network intrusion detection

datasets. Journal of Big Data, 8(1), 1–41. https://doi.org/10.1186/s40537-020-00390-x

Chiche, A., & Meshesha, M. (2021). Towards a Scalable and Adaptive Learning Approach for

Network Intrusion Detection. Journal of Computer Networks and Communications, 2021. https://doi.org/10.1155/2021/8845540

Di Mauro, M., Galatro, G., Fortino, G., & Liotta, A. (2021). Supervised feature selection

techniques in network intrusion detection: A critical review. Engineering Applications of Artificial Intelligence, 101, 104216–. https://doi.org/10.1016/j.engappai.2021.104216

Djenna, A., Harous, S., & Saidouni, D. E. (2021). Internet of Things Meet Internet of Threats:

New Concern Cyber Security Issues of Critical Cyber Infrastructure. Applied Sciences, 11(10), 4580–. https://doi.org/10.3390/app11104580

Dixit, P., Kohli, R., Acevedo-Duque, A., Gonzalez-Diaz, R. R., & Jhaveri, R. H. (2021).

Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection. Security and Communication Networks, 2021, 1–23. https://doi.org/10.1155/2021/5561816

Gopal, S. ., Poongodi, C., Nanthiya, D., Snega Priya, R., Saran, G., & Sathya Priya, M. (2021).

Mitigating DoS attacks in IoT using Supervised and Unsupervised Algorithms – A Survey. IOP Conference Series. Materials Science and Engineering, 1055(1), 12072–. https://doi.org/10.1088/1757-899X/1055/1/012072