Stakeholder analysis of the governance framework of a national SDI dataset-whose needs are met in the buildings and address register of the Netherlands?

National spatial data infrastructures are key to achieving the Digital Earth vision.In many cases,national datasets are integrated from local datasets created and maintained by municipalities.Examples are address,building and topographic information.Integration of local datasets may result in a dataset satisfying the needs of users of national datasets,but is it productive for those who create and maintain the data?This article presents a stakeholder analysis of the Basisregistratie Adressen en Gebouwen(BAG),a collection of base information about addresses and buildings in the Netherlands.The information is captured and maintained by municipalities and integrated into a national base register by Kadaster,the Cadastre,Land Registry and Mapping Agency of the Netherlands.The stakeholder analysis identifies organisations involved in the BAG governance framework,describes their interests,rights,ownerships and responsibilities in the BAG,and maps the relationships between them.Analysis results indicate that Kadaster and the municipalities have the highest relative importance in the governance framework of the BAG.The study reveals challenges of setting up a governance framework that maintains the delicate balance between the interests of all stakeholders.The results provide guidance for SDI role players setting up governance frameworks for national or global datasets.

Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.