TY - GEN
T1 - LSTM-GRU Based Efficient Intrusion Detection in 6G-Enabled Metaverse Environments
AU - Gupta, Brij B.
AU - Gaurav, Akshat
AU - Arya, Varsha
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - In response to the growing security demands of the 6G-enabled Metaverse, this paper introduces an efficient intrusion detection system utilizing a novel LSTM-GRU-based neural network. The model capitalizes on the sequential data processing prowess of LSTM and GRU layers to discern complex patterns indicative of cyber threats. Evaluated on a diverse dataset, the model architecture demonstrated significant accuracy improvements over traditional machine learning methods, achieving a 0.90 overall accuracy with high precision across various attack classes. Our results, presented through loss and accuracy trends, confusion matrices, and classification reports, attest to the model's robustness and generalizability. This study proposes a promising approach to safeguarding the Metaverse-6G convergence, marking a step forward in predictive cybersecurity measures.
AB - In response to the growing security demands of the 6G-enabled Metaverse, this paper introduces an efficient intrusion detection system utilizing a novel LSTM-GRU-based neural network. The model capitalizes on the sequential data processing prowess of LSTM and GRU layers to discern complex patterns indicative of cyber threats. Evaluated on a diverse dataset, the model architecture demonstrated significant accuracy improvements over traditional machine learning methods, achieving a 0.90 overall accuracy with high precision across various attack classes. Our results, presented through loss and accuracy trends, confusion matrices, and classification reports, attest to the model's robustness and generalizability. This study proposes a promising approach to safeguarding the Metaverse-6G convergence, marking a step forward in predictive cybersecurity measures.
KW - 6G Network Technology
KW - Intrusion Detection Systems
KW - LSTM-GRU Neural Networks
KW - Metaverse Cybersecurity
KW - Sequential Data Processing
UR - https://www.scopus.com/pages/publications/85198901835
U2 - 10.1109/WoWMoM60985.2024.00032
DO - 10.1109/WoWMoM60985.2024.00032
M3 - Conference contribution
AN - SCOPUS:85198901835
T3 - Proceedings - 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2024
SP - 118
EP - 123
BT - Proceedings - 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2024
T2 - 25th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2024
Y2 - 4 June 2024 through 7 June 2024
ER -