We are thrilled to announce that our lab’s latest paper, “Evaluating the Robustness of DL-Based AAD in SDN-IoT Networks Against OOD Data and Poisoning Attacks Using Autoencoders,” has been accepted for publication in the Springer Journal of Network and Systems Management (Special Issue: Cyber-Security in Software-defined and Virtualized Infrastructures). Congratulate Tharindu!
This work explores the robustness of deep learning–based Autonomous anomaly detection systems in SDN-IoT networks against out-of-distribution data and poisoning attacks. Our findings show that denoising autoencoders offer improved resilience in securing dynamic, large-scale SDN-IoT environments.
Explore more at this link.