
Research Article
Creating a Protected Virtual Learning Space: A Comprehensive Strategy for Security and User Experience in Online Education
@INPROCEEDINGS{10.1007/978-3-031-48888-7_30, author={Mohan Sai Dinesh Boddapati and Sri Aravind Desamsetti and Karunasri Adina and Padma Jyothi Uppalapati and P T Satyanarayana Murty and RajaRao P. B. V}, title={Creating a Protected Virtual Learning Space: A Comprehensive Strategy for Security and User Experience in Online Education}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={Intrusion detection Abusive messages BERT model}, doi={10.1007/978-3-031-48888-7_30} }
- Mohan Sai Dinesh Boddapati
Sri Aravind Desamsetti
Karunasri Adina
Padma Jyothi Uppalapati
P T Satyanarayana Murty
RajaRao P. B. V
Year: 2024
Creating a Protected Virtual Learning Space: A Comprehensive Strategy for Security and User Experience in Online Education
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_30
Abstract
The pandemic has a significant impact on how people conduct meetings, both in corporations and in schools. Online meetings have become a popular way to connect people from all over the world, lowering the expenses and time associated with travel. Various video conferencing systems and communication tools have aided in this trend towards online meetings. Many countries have moved to online classrooms as an alternative to traditional face-to-face instruction in the educational sector. It has also enabled educational institutions to adapt to changing circumstances and continue to educate students. One of the major concerns is security. As online platforms become more popular, the potential of infiltration activities such as hacking or unauthorized access increases. This study proposes a comprehensive strategy for improving security and user experience in online education. The framework focuses on detecting existing participants, detecting intruders, restricting intruders, and restricting abusive messages. It employs authentication mechanisms, user behaviour analysis, network monitoring, and machine learning algorithms to validate participant identities, differentiate legitimate users from prospective invaders, restrict unauthorized access, and promote courteous conversation. The framework proves its usefulness in minimizing security concerns and promoting a secure online learning environment through simulations and case studies.