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International Conference on Management of data (SIGMOD)

作者： Kalavri, Vasiliki Salihoglu, Semih Boston Univ Boston MA 02215 USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781450392495

GRADES-NDA is the premier workshop on graph data management and analytics that aims to bring together researchers from academia, industry, and government. GRADES-NDA'22 is a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards addressing domain specific challenges or handling noise in real-world graphs. In 2022, GRADES-NDA is in its fifth edition.

关键词： graph data management network data analytics

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GRADES-NDA'24: 7th Joint Workshop on Graph data Management Experiences & Systems (GRADES) and network data analytics (NDA) 24

GRADES-NDA'24: 7th Joint Workshop on Graph Data Management E...

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ACM International Conference on Management of data (SIGMOD)

作者： Hartig, Olaf Kaoudi, Zoi Amazon Web Serv Stockholm Sweden Linkoping Univ Linkoping Sweden IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9798400704222

GRADES-NDA is the premier workshop series on graph data management and analytics that aims to bring together researchers from academia, industry, and governmental organizations. GRADES-NDA'24 is a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as proposing and discussing novel methods and techniques for addressing domain-specific challenges. In 2024, GRADES-NDA is in its seventh edition.

关键词： graph data management network data analytics

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Unsupervised Detection of Adversarial Collaboration in data-Driven networking 10

Unsupervised Detection of Adversarial Collaboration in Data-...

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10th IEEE International Conference on networks of the Future (NoF)

作者： Sammarco, Matteo Mitre Campista, Miguel Elias Detyniecki, Marcin Razafindralambo, Tahiry de Amorim, Marcelo Dias AXA Paris France Univ Fed Rio de Janeiro GTA COPPE PEEDel Poli Rio De Janeiro RJ Brazil Polish Acad Sci Krakow Poland Univ La Reunion LIM St Denis Reunion France Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781728144450

data-driven networking in combination with machine learning is a powerful way to design and manage networked systems. In this paper, we consider the case of participatory collection of wireless traffic, which is an inexpensive way to infer the wireless activity in a locality. Since such a type of measurement system leans on the goodwill of the end users, it opens a new venue for malicious actions. Possible consequences of attacks are changes in the underlying communication substrate or even the collapse of the network. We assess the influence of these adversaries by identifying possible hostile actions and propose a method to detect them based on unsupervised machine learning models. Through an experimental campaign in various scenarios, we show that attacks with critical impacts are systematically detected, while unidentified attacks produce only a negligible impact in the measurement system.

关键词： network data analytics data-driven networking wireless networks collaborative measurements, and machine learning in network measurement

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network System of Systems Manager 24

Network System of Systems Manager

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Integrated Communications, Navigation and Surveillance Conference (ICNS)

作者： Dobie, Joe Holder, Reginald L3Harris Technol Inc Melbourn FL 32934 USA L3Harris Technol Inc Melbourne FL USA

ISBN: (纸本)9798350393101;9798350393095

For several decades, the L3Harris Mission networks organization has perfected the design, deployment, operations, and management of enterprise-level networks for safety-critical missions using a managed service framework. L3Harris has successfully constructed geographically large and technologically diverse Local Area network (LAN)/Wide Area network (WAN) solutions for State and Federal customers by selecting, optimizing, and integrating Commercial off the Shelf (COTS) products and services into an integrated or homogeneous solution meeting exacting performance requirements. These purpose-built network solutions ensure performance and resilience through tailored provisioning and the configuration of highly reliable (e.g., through adaptive redundancy) architectures, diverse service connections and equipment, ubiquitous bandwidth allocation and an unparalleled telecommunication provider governance by a centralized operational support system. Over this same period, there have been key advances in the networking industry which signal the need to evolve beyond our centralized managed services model. These advances include the rapid industry transition from a dedicated Time-division Multiple Access (TDMA)-based solution to multifunction IP-based networks with Quality of Service (QoS) support, the growing deployment of Software Defined Everything (SDE), AI-assisted advanced modeling and simulation, near-universal adoption of cloud-based Infrastructure as a Service (IaaS), design patterns and deployment solutions with the increasing presence of wireless services such as 5G (both fixed and mobile), Long Term Evolution (LTE), Geostationary Orbit (GEO), Low Earth Orbit (LEO) and Medium Earth Orbit (MEO). Within these evolving technologies, network management solutions can move from centralized to federated architectures with the promise of effortless scalability, dynamic adaptability, and seamless collaboration. A federated hierarchical architecture offers organizations a blue

关键词： network Automation Machine Learning Autonomous networking network data analytics Situational Awareness

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An NWDAF Approach to 5G Core network Signaling Traffic: Analysis and Characterization

An NWDAF Approach to 5G Core Network Signaling Traffic: Anal...

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IEEE Global Communications Conference (GLOBECOM)

作者： Manias, Dimitrios Michael Chouman, Ali Shami, Abdallah Western Univ ECE Dept London ON Canada

ISBN: (纸本)9781665435406

data-driven approaches and paradigms have become promising solutions to efficient network performances through optimization. These approaches focus on state-of-the-art machine learning techniques that can address the needs of 5G networks and the networks of tomorrow, such as proactive load balancing. In contrast to model-based approaches, data-driven approaches do not need accurate models to tackle the target problem, and their associated architectures provide a flexibility of available system parameters that improve the feasibility of learning-based algorithms in mobile wireless networks. The work presented in this paper focuses on demonstrating a working system prototype of the 5G Core (5GC) network and the network data analytics Function (NWDAF) used to bring the benefits of data-driven techniques to fruition. Analyses of the network-generated data explore core intra-network interactions through unsupervised learning, clustering, and evaluate these results as insights for future opportunities and works.

关键词： NWDAF 5G Core networks Next-Generation networks network data analytics

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FENet: Roles Classification of IP Addresses Using Connection Patterns 2

FENet: Roles Classification of IP Addresses Using Connection...

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2nd IEEE International Conference on Information and Computer Technologies (ICICT)

作者： Du, Fei Zhang, Yongzheng Bao, Xiuguo Liu, Boyuan Univ Chinese Acad Sci Sch Cyber Secur Chinese Acad Sci Inst Informat Engn Beijing Peoples R China CNCERT CC Natl Internet Emergency Ctr Beijing Peoples R China

ISBN: (纸本)9781728133232

It is valuable to classify IP address roles based on network traffic behavior for network security analysis. Many previous studies have focused on coarse-grained classification (e.g., servers, clients and P2P, and so on.), these do not meet the increasingly diverse needs of applications. In this paper, we propose a novel approach for learning the continuous feature representation of connection patterns that we call FENet, which focuses on the low-dimensional embedding of IP address connection features. Thus, we trained two-tier neural networks that classified IP address roles in the given network dataset. Our approach can achieve more fine granularity representation and classification of IP address roles. Experimental results demonstrate the effectiveness of FENet over existing state-of-the-art techniques. In several real-world networks from active IP addresses, we have achieved very high classification accuracy and stability.

关键词： network data analytics network security feature learning roles classification

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Anomaly Detection Employing a 5G Core data analytics Framework 13

Anomaly Detection Employing a 5G Core Data Analytics Framewo...

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13th IEEE International Conference on Cloud networking, CloudNet 2024

作者： Oliveira, Junia Maisa Almeida, Jonatan De Britto E Silva, Erik Rodrigues Moreira, Larissa Ferreira Moreira, Rodrigo Silva, Flavio O. MacEdo, Daniel F. Nogueira, Jose Marcos Universidade Federal de Minas Computer Science Department Gerais Brazil University of Bologna Department of Electrical Electronic and Information Engineering Italy Universidade Federal de Minas Electrical Engineering Department Gerais Brazil Brazil Department of Informatics Portugal

ISBN: (纸本)9798350376562

The network data analytics Function (NWDAF) within the 5G core is not an inherent feature of open-source 5G cores, making its implementation necessary based on provider demands. However, for those seeking to integrate NWDAF into the core but lacking expertise in core architecture or application programming interface (API) integration, this task can be daunting, potentially resulting in the abandonment of data analytics implementation efforts. While the current version of NWDAF specifies certain use cases, it does not provide alternatives for developing new analytics scenarios. To fill this gap, this paper proposes a framework for 5G network data analytics designed to facilitate algorithmic modifications and the addition of new analytical contexts. The framework was developed considering the 3GPP technical specifications related to data analytics. Its evaluation was performed through a use case focused on cybersecurity anomaly detection applied to a 5G network core. The results demonstrated that the framework simplifies installation, increases flexibility for new algorithms, and integrates seamlessly into the 5G network core, effectively addressing previously identified shortcomings. © 2024 IEEE.

关键词： 3GPP 5G core 5G network anomaly detection data analytics network data analytics NWDAF

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A data-Driven Multiobjective Optimization Framework for Hyperdense 5G network Planning

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IEEE ACCESS 2020年 8卷 169423-169443页

作者： Haile, Beneyam Berehanu Mutafungwa, Edward Hamalainen, Jyri Aalto Univ Sch Elect Engn Dept Commun & Networking Espoo 02150 Finland

The trials and rollout of the fifth generation (5G) network technologies are gradually intensifying as 5G is positioned as a platform that not only accommodates exploding data traffic but also unlocks a multitude use cases, services and deployment scenarios. However, the need for hyperdense 5G deployments is revealing some of the limitations of planning approaches that hitherto proved adequate for pre-5G systems. The hyperdensification envisioned in 5G networks not only adds complexity to network planning and optimization problems, but underlines need for more realistic data-driven approaches that consider cost, varying demands and other contextual attributes to produce feasible topologies. Furthermore, the quest for network programmability and automation including the 5G radio access network (RAN), as manifested by network slicing technologies and more flexible RAN architectures, are also among other factors that influence planning and optimization frameworks. Collectively, these deployment trends, technological developments and evolving (and diverse) service demands point towards the need for more holistic frameworks. This article proposes a data-driven multiobjective optimization framework for hyperdense 5G network planning with practical case studies used to illustrate added value compared to contemporary network planning and optimization approaches. Comparative results from the case study with real network data reveal potential performance and cost improvements of hyperdense optimized networks produced by the proposed framework due to increased use of contextual data of planning area and focus on objectives that target demand satisfaction.

关键词： Planning 5G mobile communication Optimization Market research Long Term Evolution Radio access networks Macrocell networks 5G hyperdense networks network planning techno-economics network data analytics multiobjective optimization small cells Cloud-RAN RAN slicing

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