An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning

• URL: http://arxiv.org/abs/2410.05312v1
• Date: Fri, 4 Oct 2024 21:12:23 GMT
• Title: An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning
• Authors: Rodrigo Moreira, Rodolfo S. Villaca, Moises R. N. Ribeiro, Joberto S. B. Martins, Joao Henrique Correa, Tereza C. Carvalho, Flavio de Oliveira Silva,
• Abstract summary: We propose an architecture-intelligent security mechanism to improve the Network Slicing solutions. We identify Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-native telemetry records.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial Internet of Things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture-intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machine learning, providing intra-slice and architectural operation security for the Slicing Future Internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving an average accuracy of approximately $95.60\%$ in the network slicing architecture and $99.99\%$ for the deployed slice -- intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures.

Related papers

• Intelligent Data-Driven Architectural Features Orchestration for Network Slicing [0.0]
  Orchestration and machine learning are key elements with a crucial role in the network-slicing processes. This paper discusses machine learning-based orchestration of features and capabilities in network slicing architectures. An attack prevention security mechanism is developed for the SFI2 architecture using distributed embedded and cooperating ML agents.
  arXiv  Detail & Related papers  (2024-01-12T12:32:36Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Serving Deep Learning Model in Relational Databases [70.53282490832189]
  Serving deep learning (DL) models on relational data has become a critical requirement across diverse commercial and scientific domains. We highlight three pivotal paradigms: The state-of-the-art DL-centric architecture offloads DL computations to dedicated DL frameworks. The potential UDF-centric architecture encapsulates one or more tensor computations into User Defined Functions (UDFs) within the relational database management system (RDBMS)
  arXiv  Detail & Related papers  (2023-10-07T06:01:35Z)
• Enhancing Network Slicing Architectures with Machine Learning, Security, Sustainability and Experimental Networks Integration [0.21200026734831154]
  Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications. NS architecture proposals typically fulfill the needs of specific sets of domains with commonalities.
  arXiv  Detail & Related papers  (2023-07-18T11:22:31Z)
• HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection [0.0]
  We propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.
  arXiv  Detail & Related papers  (2022-04-08T19:06:16Z)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• Intelligent Zero Trust Architecture for 5G/6G Tactical Networks: Principles, Challenges, and the Role of Machine Learning [4.314956204483074]
  We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components. This paper presents the architectural design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks.
  arXiv  Detail & Related papers  (2021-05-04T13:14:29Z)
• Symbolic Reinforcement Learning for Safe RAN Control [62.997667081978825]
  We show a Symbolic Reinforcement Learning (SRL) architecture for safe control in Radio Access Network (RAN) applications. In our tool, a user can select a high-level safety specifications expressed in Linear Temporal Logic (LTL) to shield an RL agent running in a given cellular network. We demonstrate the user interface (UI) helping the user set intent specifications to the architecture and inspect the difference in allowed and blocked actions.
  arXiv  Detail & Related papers  (2021-03-11T10:56:49Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.