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In the scenarios of specific conditions and crises such as the coronaviruspandemic, the availability of e-learning ecosystem elements is furtherhighlighted. The growing importance for securing such an ecosystem can be seenfrom DDoS (Distributed Denial of Service) attacks on e-learning components ofthe Croatian e-learning system. The negative impact of the conducted attack isvisible in numerous users who were prevented from participating in andimplementing the planned teaching process. Network anomalies such as conductedDDoS attacks were identified as one of the crucial threats to the e-learningsystems. In this paper, an overview of the network anomaly phenomenon was givenand botnets' role in generating DDoS attacks, especially IoT device impact. Thepaper analyzes the impact of the COVID-19 pandemic on the e-learning systems inCroatia. Based on the conclusions, a research methodology has been proposed todevelop a cyber-threat detection model that considers the specifics of theapplication of e-learning systems in crisis, distinguishing flash crowd eventsfrom anomalies in the communication network. The proposed methodology includesestablishing a theoretical basis on DDoS and flash crowd event traffic,defining a laboratory testbed setup for data acquisition, development of DDoSdetection model, and testing the applicability of the developed model on thecase study. The implementation of the proposed methodology can improve thequality of the teaching process through timely DDoS detection and it givesother socio-economic contributions such as developing a specific researchdomain, publicly available dataset of network traffic, and raising thecyber-security of the e-learning systems.

Methodology for Detecting Cyber Intrusions in e-Learning Systems during COVID-19 Pandemic