Experimental Comparison of ML/DL Approaches for Cyberattacks Diagnostics

Author:

Krivchenkov AleksandrORCID,Misnevs BorissORCID,Grakovski AlexanderORCID

Publisher

Springer International Publishing

Reference12 articles.

1. ENISA European Union Agency for Network and Information Security (ENISA): Threat Landscape Report 2018, 15 Top Cyberthreats and Trends (2019). https://doi.org/10.2824/622757. https://www.enisa.europa.eu. ISBN 978-929204-286-8, ISSN 2363-3050

2. Muniz, J., Lakhani, A.: Investigating the Cyber Breach: The Digital Forensics Guide for the Network Engineer. Pearson Education Inc, Indianapolis (2018)

3. Abdelhameed, M.: Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic. A thesis for the degree of Doctor of Philosophy. University of New South Wales, Australia, June 2017. https://www.researchgate.net/publication/328784548_Designing_an_online_and_reliable_statistical_anomaly_detection_framework_for_dealing_with_large_high-speed_network_traffic#fullTextFileContent. Accessed 05 July 2020

4. Al-Garadi, M., Mohamed, A., Al-Ali, A., Du, X., Guizani, M.: A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018). https://www.researchgate.net/publication/326696402_A_Survey_of_Machine_and_Deep_Learning_Methods_for_Internet_of_Things_IoT_Security. Accessed 05 July 2020

5. Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2) (2016). https://www.academia.edu/33112124/Data_Mining_and_Machine_Learning_Methods_for_Cyber_Security_Intrusion_Detection. Accessed 05 July 2020

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