Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
Document Type
Event
Start Date
15-4-2025 4:00 PM
Description
Advanced networking technology faces challenges with diverse usage, especially packet loss. Researchers tried deep learning to predict losses, but these black-box methods cannot explain the correlation between packet loss and parameters or mitigate losses. We propose a deep learning model to reconstruct lost packets in a complex networking scenario while integrating an explainable AI approach to explain the correlation between the networking parameters and the packet loss.. Integrating an elementary networking simulation designed in the ns2 platform, we collected data about networking packets and their associated parameters, based on which we trained and tested our deep learning model. Our approach was tested with 5-fold cross-validation, showing a mean accuracy of 79.09% for reconstructing the lost packets when maintaining a noticeable packet delivery fraction (PDF) rate of 98.9%, showing the effective performance of our proposed framework.
GRP-053 EXPAND: Explainable AI Integrated Deep Learning-based Reconstruction of the Lost Packets
https://www.kennesaw.edu/ccse/events/computing-showcase/sp25-cday-program.php
Advanced networking technology faces challenges with diverse usage, especially packet loss. Researchers tried deep learning to predict losses, but these black-box methods cannot explain the correlation between packet loss and parameters or mitigate losses. We propose a deep learning model to reconstruct lost packets in a complex networking scenario while integrating an explainable AI approach to explain the correlation between the networking parameters and the packet loss.. Integrating an elementary networking simulation designed in the ns2 platform, we collected data about networking packets and their associated parameters, based on which we trained and tested our deep learning model. Our approach was tested with 5-fold cross-validation, showing a mean accuracy of 79.09% for reconstructing the lost packets when maintaining a noticeable packet delivery fraction (PDF) rate of 98.9%, showing the effective performance of our proposed framework.
