Application of Deep Learning-Based Malware Detection in Video Conferencing Systems
Weitao Wang, Lei Lei, Lisong Shao, Wang Peng, Jiale Chang · 2024
This paper explores the application of deep learning-based malware detection models in video conferencing systems. By constructing a large-scale dataset of malware and training deep neural network models such as CNN, RNN, and LSTM, high-precision detection of malware is achieved, outperforming traditional machine learning models. Integrating this deep learning model into the video conferencing system, a comprehensive malware defense system is built, effectively blocking a large number of attacks through real-time monitoring and intelligent analysis, significantly improving the security of video conferencing. The research results demonstrate that deep learning technology has broad prospects for application in this field, providing strong support for building a more secure video conferencing environment.