Moving Target Defense based Secured Network Slicing System in the O-RAN Architecture

• URL: http://arxiv.org/abs/2309.13444v1
• Date: Sat, 23 Sep 2023 18:21:33 GMT
• Title: Moving Target Defense based Secured Network Slicing System in the O-RAN Architecture
• Authors: Mojdeh Karbalaee Motalleb, Chafika Benzaïd, Tarik Taleb, Vahid Shah-Mansouri,
• Abstract summary: Artificial intelligence (AI) and machine learning (ML) security threats can even threaten open radio access network (O-RAN) benefits. This paper proposes a novel approach to estimating the optimal number of predefined VNFs for each slice. We also address secure AI/ML methods for dynamic service admission control and power minimization in the O-RAN architecture.
• Score: 12.360792257414458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The open radio access network (O-RAN) architecture's native virtualization and embedded intelligence facilitate RAN slicing and enable comprehensive end-to-end services in post-5G networks. However, any vulnerabilities could harm security. Therefore, artificial intelligence (AI) and machine learning (ML) security threats can even threaten O-RAN benefits. This paper proposes a novel approach to estimating the optimal number of predefined VNFs for each slice while addressing secure AI/ML methods for dynamic service admission control and power minimization in the O-RAN architecture. We solve this problem on two-time scales using mathematical methods for determining the predefined number of VNFs on a large time scale and the proximal policy optimization (PPO), a Deep Reinforcement Learning algorithm, for solving dynamic service admission control and power minimization for different slices on a small-time scale. To secure the ML system for O-RAN, we implement a moving target defense (MTD) strategy to prevent poisoning attacks by adding uncertainty to the system. Our experimental results show that the proposed PPO-based service admission control approach achieves an admission rate above 80\% and that the MTD strategy effectively strengthens the robustness of the PPO method against adversarial attacks.

Related papers

• PRM-Free Security Alignment of Large Models via Red Teaming and Adversarial Training [0.5439020425819]
  Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet they pose significant security risks that threaten their safe deployment in critical domains.<n>This paper presents a novel PRM-free security alignment framework that leverages automated red teaming and adversarial training to achieve robust security guarantees while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-07-14T17:41:12Z)
• ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes [0.0]
  ADA operates by continuously and automatically rotating these workloads at the infrastructure level.<n>This constant managed churn invalidates attacker assumptions and disrupts potential kill chains.<n>We posit that ADA's novel approach to AMTD provides a more robust, agile, and operationally efficient zero-trust model for AI services.
  arXiv  Detail & Related papers  (2025-05-27T02:24:45Z)
• Integrated LLM-Based Intrusion Detection with Secure Slicing xApp for Securing O-RAN-Enabled Wireless Network Deployments [2.943640991628177]
  The Open Radio Access Network (O-RAN) architecture is reshaping telecommunications by promoting openness, flexibility, and intelligent closed-loop optimization. This research explores using large language models (LLMs) to generate security recommendations based on the temporal traffic patterns of connected UEs.
  arXiv  Detail & Related papers  (2025-04-01T01:45:07Z)
• Robust Intrusion Detection System with Explainable Artificial Intelligence [0.0]
  Adversarial input can exploit machine learning (ML) models through standard interfaces. Conventional defenses such as adversarial training are costly in computational terms and often fail to provide real-time detection. We suggest a novel strategy for detecting and mitigating adversarial attacks using eXplainable Artificial Intelligence (XAI)
  arXiv  Detail & Related papers  (2025-03-07T10:31:59Z)
• Towards Secure Intelligent O-RAN Architecture: Vulnerabilities, Threats and Promising Technical Solutions using LLMs [12.016792293867278]
  Open radio access network (O-RAN) is a new concept defining an intelligent architecture with enhanced flexibility, openness, and the ability to slice services more efficiently. In this paper, we present an in-depth security analysis of the O-RAN architecture. We discuss the potential threats that may arise in the different O-RAN architecture layers and their impact on the Confidentiality, Integrity, and Availability (CIA) triad.
  arXiv  Detail & Related papers  (2024-11-13T14:31:52Z)
• Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks [0.9437165725355702]
  We build a resilient edge machine learning architecture that is designed to withstand adversarial attacks. The reML is based on the Resilient DDDAS paradigm, Moving Target Defense (MTD) theory, and TinyML. The proposed approach is power-efficient and privacy-preserving and, therefore, can be deployed on power-constrained devices to enhance ICS security.
  arXiv  Detail & Related papers  (2024-09-26T19:37:37Z)
• Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach [51.63921041249406]
  Non-orthogonal multiple access (NOMA) enables multiple users to share the same frequency band, and simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) deploying STAR-RIS indoors presents challenges in interference mitigation, power consumption, and real-time configuration. A novel network architecture utilizing multiple access points (APs), STAR-RISs, and NOMA is proposed for indoor communication.
  arXiv  Detail & Related papers  (2024-06-19T07:17:04Z)
• Enhancing O-RAN Security: Evasion Attacks and Robust Defenses for Graph Reinforcement Learning-based Connection Management [5.791956438741676]
  We study various attacks and defenses on machine learning (ML) models in Open Radio Access Networks (O-RAN) A comprehensive modeling of the security threats and the demonstration of adversarial attacks and defenses is still in its nascent stages. We develop and demonstrate robust training-based defenses against the challenging physical/jamming-based attacks and show a 15% improvement in the coverage rates when compared to employing no defense over a range of noise budgets.
  arXiv  Detail & Related papers  (2024-05-06T22:27:24Z)
• Practical Adversarial Attacks Against AI-Driven Power Allocation in a Distributed MIMO Network [0.0]
  In distributed multiple-input multiple-output (D-MIMO) networks, power control is crucial to optimize the spectral efficiencies of users. Deep neural network based artificial intelligence (AI) solutions are proposed to decrease the complexity. In this work, we show that threats against the target AI model which might be originated from malicious users or radio units can substantially decrease the network performance.
  arXiv  Detail & Related papers  (2023-01-23T07:51:25Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach [67.06539298956854]
  fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs)
  arXiv  Detail & Related papers  (2022-06-13T02:19:20Z)
• Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach [102.00221938474344]
  A novel model is proposed for the coverage and capacity optimization of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) assisted networks. A loss function-based update strategy is the core point, which is able to calculate weights for both loss functions of coverage and capacity by a min-norm solver at each update. The numerical results demonstrate that the investigated update strategy outperforms the fixed weight-based MO algorithms.
  arXiv  Detail & Related papers  (2022-04-13T13:52:22Z)
• Adversarial Machine Learning Threat Analysis in Open Radio Access Networks [37.23982660941893]
  The Open Radio Access Network (O-RAN) is a new, open, adaptive, and intelligent RAN architecture. In this paper, we present a systematic adversarial machine learning threat analysis for the O-RAN.
  arXiv  Detail & Related papers  (2022-01-16T17:01:38Z)
• 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)
• Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization [76.51980153902774]
  Federated learning (FL) is vulnerable to external attacks on FL models during parameters transmissions. In this paper, we propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms.
  arXiv  Detail & Related papers  (2021-01-28T03:28:18Z)

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.