Abstract:
This study addresses the critical need to secure VR network communication from non-immersive attacks, employing an intrusion detection system (IDS). While deep learning (...Show MoreMetadata
Abstract:
This study addresses the critical need to secure VR network communication from non-immersive attacks, employing an intrusion detection system (IDS). While deep learning (DL) models offer advanced solutions, their opacity as "black box" models raises concerns. Recognizing this gap, the research underscores the urgency for DL-based explainability, enabling data analysts and cybersecurity experts to grasp model intricacies. Leveraging sensed data from IoT devices, our work trains a DL-based model for attack detection and mitigation in the VR network, Importantly, we extend our contribution by providing comprehensive global and local interpretations of the model’s decisions post-evaluation using SHAP-based explanation.
Published in: 2023 14th International Conference on Information and Communication Technology Convergence (ICTC)
Date of Conference: 11-13 October 2023
Date Added to IEEE Xplore: 23 January 2024
ISBN Information: