Tài liệu tham khảo |
Loại |
Chi tiết |
[1] S. A. R. Shah and B. Issac, “Performance comparison of intrusion detection systems and application of machine learning to Snort system,” Futur. Gener.Comput. Syst., vol. 80, pp. 157–170, 2018, doi: 10.1016/j.future.2017.10.016 |
Sách, tạp chí |
Tiêu đề: |
Performance comparison of intrusion detectionsystems and application of machine learning to Snort system,” "Futur. Gener."Comput. Syst |
|
[2] B. Qi, M. Wu, and L. Zhang, “A DNN-based object detection system on mobile cloud computing,” 2017 17th Int. Symp. Commun. Inf. Technol. Isc |
Sách, tạp chí |
Tiêu đề: |
A DNN-based object detection system onmobile cloud computing,” |
|
[3] S. Potluri and C. Diedrich, “Accelerated deep neural networks for enhanced Intrusion Detection System,” IEEE Int. Conf. Emerg. Technol. Fact. Autom.ETFA, vol. 2016-Novem, 2016, doi: 10.1109/ETFA.2016.7733515 |
Sách, tạp chí |
Tiêu đề: |
Accelerated deep neural networks for enhancedIntrusion Detection System,” "IEEE Int. Conf. Emerg. Technol. Fact. Autom."ETFA |
|
[4] P. Chen, Y. Guo, J. Zhang, Y. Wang, and H. Hu, “A Novel Preprocessing Methodology for DNN-Based Intrusion Detection,” 2020 IEEE 6th Int. Conf.Comput. Commun. ICCC 2020, pp. 2059–2064, 2020, doi:10.1109/ICCC51575.2020.9345300 |
Sách, tạp chí |
Tiêu đề: |
A Novel PreprocessingMethodology for DNN-Based Intrusion Detection,” "2020 IEEE 6th Int. Conf."Comput. Commun. ICCC 2020 |
|
[5] S. Gamage and J. Samarabandu, “Deep learning methods in network intrusion detection: A survey and an objective comparison,” J. Netw. Comput. Appl., vol. 169, no. May, p. 102767, 2020, doi: 10.1016/j.jnca.2020.102767 |
Sách, tạp chí |
Tiêu đề: |
Deep learning methods in network intrusiondetection: A survey and an objective comparison,” "J. Netw. Comput. Appl |
|
[6] L. H. Li, R. Ahmad, W. C. Tsai, and A. K. Sharma, “A Feature Selection Based DNN for Intrusion Detection System,” Proc. 2021 15th Int. Conf.Ubiquitous Inf. Manag. Commun. IMCOM 2021, 2021, doi:10.1109/IMCOM51814.2021.9377405 |
Sách, tạp chí |
Tiêu đề: |
A Feature SelectionBased DNN for Intrusion Detection System,” "Proc. 2021 15th Int. Conf."Ubiquitous Inf. Manag. Commun. IMCOM 2021 |
|
[8] P. H. Swain and H. Hauska, “Decision Tree Classifier: Design and Potential.,” IEEE Trans Geosci Electron, vol. GE-15, no. 3, pp. 142–147, 1977, doi: 10.1109/tge.1977.6498972 |
Sách, tạp chí |
Tiêu đề: |
Decision Tree Classifier: Design andPotential.,” "IEEE Trans Geosci Electron |
|
[9] S. G. A. P. M. J. Erquiaga, “IoT-23: A labeled dataset with malicious and benign IoT network traffic”, doi: http://doi.org/10.5281/zenodo.4743746 |
Sách, tạp chí |
Tiêu đề: |
IoT-23: A labeled dataset with malicious andbenign IoT network traffic |
|
[10] Y. Zhang, P. Li, and X. Wang, “Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network,” IEEE Access, vol. 7, no. c, pp. 31711–31722, 2019, doi: 10.1109/ACCESS.2019.2903723 |
Sách, tạp chí |
Tiêu đề: |
Intrusion Detection for IoT Based onImproved Genetic Algorithm and Deep Belief Network,” "IEEE Access |
|
[11] T. M. Hoang, T. A. Pham, V. V. Do, V. N. Nguyen, and M. H. Nguyen, “A Lightweight DNN-based IDS for Detecting IoT Cyberattacks in Edge Computing,” Int. Conf. Adv. Technol. Commun., vol. 2022-Octob, pp. 136– |
Sách, tạp chí |
Tiêu đề: |
ALightweight DNN-based IDS for Detecting IoT Cyberattacks in EdgeComputing,” "Int. Conf. Adv. Technol. Commun |
|