Topic : Machine learning for cyber threats
Instructions
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).
26 References (2019,2020,2021)
Aiyanyo, I. D., Samuel, H., & Lim, H. (2020). A Systematic Review of Defensive and Offensive
Cybersecurity with Machine Learning. Applied Sciences, 10(17), 5811–.
https://doi.org/10.3390/app10175811
Akpinar, K. O., & Ozcelik, I. (2019). Analysis of Machine Learning Methods in EtherCAT-
Based Anomaly Detection. IEEE Access, 7, 184365–184374.
https://doi.org/10.1109/ACCESS.2019.2960497
Apruzzese, G., Andreolini, M., Marchetti, M., Colacino, V. G., & Russo, G. (2020). AppCon:
Mitigating Evasion Attacks to ML Cyber Detectors. Symmetry (Basel), 12(4), 653–.
https://doi.org/10.3390/sym12040653
Dasgupta, P., & Collins, J. B. (2019). A Survey of Game Theoretic Approaches for Adversarial
Machine Learning in Cybersecurity Tasks. The AI Magazine, 40(2), 31–43.
https://doi.org/10.1609/aimag.v40i2.2847
Dutta, V., Chora?, M., Pawlicki, M., & Kozik, R. (2020). A Deep Learning Ensemble for
Network Anomaly and Cyber-Attack Detection. Sensors (Basel, Switzerland), 20(16),
4583–. https://doi.org/10.3390/s20164583
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
Fang, Y., Gao, J., Liu, Z., & Huang, C. (2020). Detecting Cyber Threat Event from Twitter
Using IDCNN and BiLSTM. Applied Sciences, 10(17), 5922–.
https://doi.org/10.3390/app10175922
Georgescu, T.-M. (2020). Natural Language Processing Model for Automatic Analysis of
Cybersecurity-Related Documents. Symmetry (Basel), 12(3), 354–.
https://doi.org/10.3390/sym12030354
Hindy, H., Atkinson, R., Tachtatzis, C., Colin, J.-N., Bayne, E., & Bellekens, X. (2020).
Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection.
Electronics (Basel), 9(10), 1–. https://doi.org/10.3390/electronics9101684
Jayasinghe, U., Lee, G. M., Um, T.-W., & Shi, Q. (2019). Machine Learning Based Trust
Computational Model for IoT Services. IEEE Transactions on Sustainable Computing,
4(1), 39–52. https://doi.org/10.1109/TSUSC.2018.2839623
Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection
systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1–22.
https://doi.org/10.1186/s42400-019-0038-7
Ko, I., Chambers, D., & Barrett, E. (2019). Unsupervised learning with hierarchical feature
selection for DDoS mitigation within the ISP domain. ETRI Journal, 41(5), 574–584.
https://doi.org/10.4218/etrij.2019-0109
Koloveas, P., Chantzios, T., Alevizopoulou, S., Skiadopoulos , S., & Tryfonopoulos , C. (2021).
inTIME: A Machine Learning-Based Framework for Gathering and Leveraging Web
Data to Cyber-Threat Intelligence. Electronics (Basel), 10(7), 818–.
https://doi.org/10.3390/electronics10070818
Lee, K., & Yim, K. (2020). Cybersecurity Threats Based on Machine Learning-Based Offensive
Technique for Password Authentication. Applied Sciences, 10(4), 1286–.
https://doi.org/10.3390/app10041286
Liang, F., Hatcher, W. G., Liao, W., Gao, W., & Yu, W. (2019). Machine Learning for Security
and the Internet of Things: The Good, the Bad, and the Ugly. IEEE Access, 7, 158126–
158147. https://doi.org/10.1109/ACCESS.2019.2948912
Martina Pivarníková, Pavol Sokol, & Tomáš Bajtoš. (2020). Early-Stage Detection of Cyber
Attacks. Information (Basel), 11(560), 560–. https://doi.org/10.3390/info11120560
Rashid, M. M., Kamruzzaman, J., Hassan, M. M., Imam, T., & Gordon, S. (2020). Cyberattacks
Detection in IoT-Based Smart City Applications Using Machine Learning Techniques.
International Journal of Environmental Research and Public Health, 17(24), 9347–.
https://doi.org/10.3390/ijerph17249347
Rathore, H., Agarwal, S., Sahay, S. K., & Sewak, M. (2018). Malware Detection Using Machine
Learning and Deep Learning. Big Data Analytics, 402–411.https://doi.org/10.1007/978-3-030-04780-1_28
Roopak, M., Tian, G. Y., & Chambers, J. (2020). Multi-objective-based feature selection for
DDoS attack detection in IoT networks. IET Networks, 9(3), 120–127. https://doi.org/10.1049/iet-net.2018.5206
Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., Chen, S., Liu, D., & Li, J. (2020).
Performance Comparison and Current Challenges of Using Machine Learning
Techniques in Cybersecurity. Energies (Basel), 13(10), 2509–.
https://doi.org/10.3390/en13102509
Sornsuwit, P., & Jaiyen, S. (2019). A New Hybrid Machine Learning for Cybersecurity Threat
Detection Based on Adaptive Boosting. Applied Artificial Intelligence, 33(5), 462–482.
https://doi.org/10.1080/08839514.2019.1582861
Subroto, A., & Apriyana, A. (2019). Cyber risk prediction through social media big data
analytics and statistical machine learning. Journal of Big Data, 6(1), 1–19.
https://doi.org/10.1186/s40537-019-0216-1
Sun, T., Yang, P., Li, M., & Liao, S. (2021). An Automatic Generation Approach of the Cyber
Threat Intelligence Records Based on Multi-Source Information Fusion. Future Internet,
13(2), 40–. https://doi.org/10.3390/fi13020040
Thapa, N., Liu, Z., KC, D. B., Gokaraju, B., & Roy, K. (2020). Comparison of Machine
Learning and Deep Learning Models for Network Intrusion Detection Systems. Future
Internet, 12(10), 1–. https://doi.org/10.3390/fi12100167
Usman, M., Jan, M., He, X., & Chen, J. (2020). A Survey on Representation Learning Efforts in
Cybersecurity Domain. ACM Computing Surveys, 52(6), 1–28.
https://doi.org/10.1145/3331174
Yan, G., Li, Q., Guo, D., & Li, B. (2019). AULD: Large Scale Suspicious DNS Activities
Detection via Unsupervised Learning in Advanced Persistent Threats. Sensors (Basel,Switzerland), 19(14), 3180–. https://doi.org/10.3390/s19143180
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