TY - GEN
T1 - Deep CNN Based Anomaly Detection in Centralized Metaverse Environment
AU - Gupta, Brij B.
AU - Gaurav, Akshat
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the continually expanding area of centralized metaverse environments, safeguarding against digital threats remains a top priority. This paper presents a model based on advanced deep learning techniques, specifically a Convolutional Neural Network (CNN), designed for the purpose of identifying unusual patterns. Our model showcases remarkable performance, achieving a final accuracy of around 94.73% and minimizing the test loss to 0.206631 throughout ten training sessions. In a comparative examination, our deep CNN model surpasses traditional Logistic Regression and a Feedforward Neural Network, underscoring its ability to discern intricate patterns and adapt to the complex dynamics of metaverse data. This research contributes to bolstering security in metaverse platforms, underscoring the pivotal role of deep CNN models in confronting the complexities tied to high-dimensional data.
AB - In the continually expanding area of centralized metaverse environments, safeguarding against digital threats remains a top priority. This paper presents a model based on advanced deep learning techniques, specifically a Convolutional Neural Network (CNN), designed for the purpose of identifying unusual patterns. Our model showcases remarkable performance, achieving a final accuracy of around 94.73% and minimizing the test loss to 0.206631 throughout ten training sessions. In a comparative examination, our deep CNN model surpasses traditional Logistic Regression and a Feedforward Neural Network, underscoring its ability to discern intricate patterns and adapt to the complex dynamics of metaverse data. This research contributes to bolstering security in metaverse platforms, underscoring the pivotal role of deep CNN models in confronting the complexities tied to high-dimensional data.
KW - Anomaly Detection
KW - Convolutional Neural Network (CNN)
KW - Deep Learning
KW - Metaverse Security
UR - http://www.scopus.com/inward/record.url?scp=85189648179&partnerID=8YFLogxK
U2 - 10.1109/ANTS59832.2023.10468937
DO - 10.1109/ANTS59832.2023.10468937
M3 - Conference contribution
AN - SCOPUS:85189648179
T3 - International Symposium on Advanced Networks and Telecommunication Systems, ANTS
BT - 2023 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023
T2 - 17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023
Y2 - 17 December 2023 through 20 December 2023
ER -