@inproceedings{b5fafc2446594fbb8c1fb90676a3cb75,
title = "SentinelMet: Enhancing Metaverse Security through Deep Learning Techniques in 6G",
abstract = "With the increasing demand for robust security in the rapidly expanding metaverse within 6 G networks, advanced intrusion detection systems (IDS) are becoming essential. This paper introduces a novel hybrid intrusion detection framework that combines Gaussian Mixture Clustering with Random Forest classifiers and KMeans Clustering with Random Forest classifiers. These models are evaluated for their effectiveness in identifying complex intrusion patterns. The hybrid approaches demonstrated high detection rates, with the Gaussian Mixture Clustering ensemble achieving a detection rate of 99.5-99.6\%, while the KMeans clustering model achieved a detection rate of \{9 8 - 9 9 \%\}. Comparative analysis shows that these models enhance detection performance while maintaining robustness in a dynamic and diverse metaverse landscape.",
keywords = "Anomaly Detection, Clustering, Convolutional Neural Networks, Deep Learning, Ensemble Methods, Hybrid Models, Intrusion Detection System, Metaverse, Predictive Security",
author = "Dikshant Rajput and Singh, \{Sunil K.\} and Sudhakar Kumar and Harshit Vashisht and Chui, \{Kwok Tai\} and Gupta, \{Brij B.\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024 ; Conference date: 15-12-2024 Through 18-12-2024",
year = "2024",
doi = "10.1109/ANTS63515.2024.10898831",
language = "English",
series = "International Symposium on Advanced Networks and Telecommunication Systems, ANTS",
booktitle = "2024 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024",
}