Predicting and Preventing Cyber Attacks during COVID-19 Time Using Data Analysis and Proposed Secure IoT layered Model
2020 4th International Conference on Multimedia Computing, Networking and Applications, MCNA 2020
The global spread of the COVID-19 pandemic and its unprecedented impact not only on health and economy but almost on all aspects of our lives, including how we work, meet, communicate, collaborate, etc. Unfortunately, these changes and the transition to the virtual space in such a short time without proper planning created opportunities for bad actors in cyberspace. In the last few months, we have witnessed new treads and waves of cyber-Attacks targeting businesses, governments, health, and other critical services. Attackers try to take advantage of people's fear of the virus, vulnerabilities associated with data collection sensors and IoT devices, and eagerness to look for solutions or protections. In this study, we will survey the nature of cyberattacks related to the COVID-19 outbreak. Them, we will analyze related data to phishing attacks using Neural Networks. This analysis is covering different technical and socio-economical aspects. We will also evaluate states' countermeasures in response to such attacks. We propose a new IoT model. We define three layers; End User, Device or Sensors, and Cloud. We can combine the proposed model with the security and privacy policies to countermeasure the cybersecurity threats facing each layer.
ANN, Cloud, COVID-19, Data Analysis, IoT model, Security attacks
Lo'Ai Tawalbeh, Fadi Muheidat, Mais Tawalbeh, Muhannad Quwaider, and Gokay Saldamli. "Predicting and Preventing Cyber Attacks during COVID-19 Time Using Data Analysis and Proposed Secure IoT layered Model" 2020 4th International Conference on Multimedia Computing, Networking and Applications, MCNA 2020 (2020): 113-118. https://doi.org/10.1109/MCNA50957.2020.9264301