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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 of the Croatian e-learning system. The negative impact of the conducted attack is visible in numerous users who were prevented from participating in and implementing the planned teaching process. Network anomalies such as conducted DDoS attacks were identified as one of the crucial threats to the e-learning systems. In this paper, an overview of the network anomaly phenomenon was given and botnets' role in generating DDoS attacks, especially IoT device impact. The paper analyzes the impact of the COVID-19 pandemic on the e-learning systems in Croatia. Based on the conclusions, a research methodology has been proposed to develop a cyber-threat detection model that considers the specifics of the application of e-learning systems in crisis, distinguishing flash crowd events from anomalies in the communication network. The proposed methodology includes establishing a theoretical basis on DDoS and flash crowd event traffic, defining a laboratory testbed setup for data acquisition, development of DDoS detection model, and testing the applicability of the developed model on the case study. The implementation of the proposed methodology can improve the quality of the teaching process through timely DDoS detection and it gives other socio-economic contributions such as developing a specific research domain, publicly available dataset of network traffic, and raising the cyber-security of the e-learning systems. [ABSTRACT FROM AUTHOR]

The problem of detecting anomalies in network traffic caused by the distributed denial of service (DDoS) attack so far has mainly been investigated in terms of detection of illegitimate DDoS traffic generated by conventional terminal devices (PCs, laptops, mobile devices, tablets, servers). Technological development has resulted in the emergence of the Internet of Things (IoT) concept, whose implementation implies numerous terminal devices with a low level of implemented protection. The large growth and prediction of future growth is noticeable in the environment of a smart home and smart office. IoT devices in such environments are increasingly being used as a platform for generating DDoS traffic due to its numeracy and low level of protection. The aim of this research is to propose a novel approach for detection of DDoS traffic generated by IoT devices in a form of conceptual network anomaly detection model. Proposed conceptual model is based on device classes which are dependent on individual device traffic characteristics. [ABSTRACT FROM AUTHOR]