Cargando…
Methodology for Detecting Cyber Intrusions in e-Learning Systems during COVID-19 Pandemic
In the scenarios of specific conditions and crises such as the coronavirus pandemic, the availability of e-learning ecosystem elements is further highlighted. The growing importance for securing such an ecosystem can be seen from DDoS (Distributed Denial of Service) attacks on e-learning components...
Autores principales: | Cvitić, Ivan, Peraković, Dragan, Periša, Marko, Jurcut, Anca D. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8179957/ http://dx.doi.org/10.1007/s11036-021-01789-3 |
Ejemplares similares
-
Correction to: Methodology for Detecting Cyber Intrusions in e-Learning Systems during COVID-19 Pandemic
por: Cvitić, Ivan, et al.
Publicado: (2021) -
Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets
por: Gyamfi, Eric, et al.
Publicado: (2022) -
Badoo Android and iOS Dating Application Analysis
por: Long, Jack, et al.
Publicado: (2022) -
A Semi-Self-Supervised Intrusion Detection System for Multilevel Industrial Cyber Protection
por: Ye, Fuchuan, et al.
Publicado: (2022) -
A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
por: Tang, Bin, et al.
Publicado: (2023)