Tootfinder

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Jack Jackman, David Ryan, Arun Narayanan, Pedro Nardelli, Indrakshi Dey
https://arxiv.org/abs/2510.11286

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Modern power grids face an acute mismatch between where data is generated and where it can be processed: protection relays, EV (Electric Vehicle) charging, and distributed renewables demand millisecond analytics at the edge, while energy-hungry workloads often sit in distant clouds leading to missed real-time deadlines and wasted power. We address this by proposing, to our knowledge, the first-ever SDEN (Software Defined Energy Network) for CaaS (Computations-as-a-Service) that unifies edge, fo…

BlockSDN: Towards a High-Performance Blockchain via Software-Defined Cross Networking optimization
Wenyang Jia, Jingjing Wang, Ziwei Yan, Xiangli Peng, Guohui Yuan
https://arxiv.org/abs/2510.08139

BlockSDN: Towards a High-Performance Blockchain via Software-Defined Cross Networking optimization
The scalability of blockchain systems is constrained by inefficient P2P broadcasting, as most existing optimizations focus only on the logical layer without considering physical network conditions. To address this, we propose BlockSDN, the first SDN-based integrated architecture for blockchain. BlockSDN employs a distributed control plane for a global network view, a graph engine for hierarchical clustering, and a hybrid macro-micro neighbor selection with hierarchical broadcasting. A dedicated…

A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network
Bilal Dalgic (Manisa Celal Bayar University, Turkey), Betul Sen (Manisa Celal Bayar University, Turkey), Muge Erel-Ozcevik (Manisa Celal Bayar University, Turkey)
https://arxiv.org/abs/2509.20068

A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network
Secure monitoring and dynamic control in an IIoT environment are major requirements for current development goals. We believe that dynamic, secure monitoring of the IIoT environment can be achieved through integration with the Software-Defined Network (SDN) and Digital Twin (DT) paradigms. The current literature lacks implementation details for SDN-based DT and time-aware intelligent model training for short-term anomaly detection against IIoT threats. Therefore, we have proposed a novel framew…

Detection of Misreporting Attacks on Software-Defined Immersive Environments
Sourya Saha, Md Nurul Absur, Shima Yousefi, Saptarshi Debroy
https://arxiv.org/abs/2509.18040 https:…

Detection of Misreporting Attacks on Software-Defined Immersive Environments
The ability to centrally control network infrastructure using a programmable middleware has made Software-Defined Networking (SDN) ideal for emerging applications, such as immersive environments. However, such flexibility introduces new vulnerabilities, such as switch misreporting led load imbalance, which in turn make such immersive environment vulnerable to severe quality degradation. In this paper, we present a hybrid machine learning (ML)-based network anomaly detection framework that ident…

Spatial Encoding of Flow Spaces for Intelligent SDN Applications
Abdur Rouf, Murat Yuksel
https://arxiv.org/abs/2509.16485 https://arxiv.org/pdf/2509.16485…

Spatial Encoding of Flow Spaces for Intelligent SDN Applications
Efficient encoding of network flow spaces while preserving spatial locality is essential for intelligent Software-Defined Networking (SDN) applications, particularly those employing reinforcement learning (RL) methods in a reactive manner. In this work, we introduce a spatially aware Bloom Filter-based approach to encode IP flow pairs, leveraging their inherent geographical locality. Through controlled experiments using IoT traffic data, we demonstrate that Bloom Filters effectively preserve sp…