engleski

Methodology Proposal for Proactive Detection of Network Anomalies in e-Learning System During the COVID-19 Scenario

In specific conditions and crisis situations such as the pandemic of coronavirus (SARS-CoV-2), or the COVID-19 disease, e-learning systems became crucial for the smooth performance of teaching and other educational processes. In such scenarios, the availability of e-learning ecosystem elements is further highlighted. An indicator of the importance of securing the availability of such an ecosystem is evident from the DDoS (Distributed Denial of Service) attack on AAI@EduHr as a key authentication service for a number of e-learning users in the Republic of Croatia. In doing so, numerous users (teachers/students/administrators) were prevented from implementing and participating in the planned teaching process. Given that DDoS as an anomaly of network traffic has been identified as one of the key threats to the e-learning ecosystem in crisis scenarios, this research focuses on the overview of methodology for developing a model for proactive detection of DDoS traffic. The challenge in detection is to effectively differentiate the increased traffic intensity and service requests caused by legitimate user activity (flash crowd) from the illegitimate traffic caused by a DDoS attack. The DDoS traffic detection model developed by the following analyzed methodology would serve as a basis for providing further guidelines and recommendations in the form of response to events that may negatively affect the availability of e-learning ecosystem elements such as DDoS attack.

availability ; cyberthreats ; DDoS ; pandemic ; SARS-CoV-2

Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)