Search Results for « Zero-Touch Network »

Abstract Background The deployment of high-fidelity diagnostic Artificial Intelligence (AI) in resource-constrained environments is hindered by the stochastic nature of network latency and bandwidth limitations. Traditional tele-radiology relies on static cloud offloading, which introduces unacceptable latency for critical care scenarios. Zero-Touch Network and Service Management (ZSM) offers a paradigm for automated network orchestration, yet current frameworks lack application-layer awareness regarding clinical urgency and image complexity. Methodology This study proposes a novel Cross-Modal Latent Transformer (CMLT) integrated within a Zero-Touch Network Orchestration architecture. The system utilizes a lightweight Edge-Gating mechanism to dynamically partition inference tasks between edge nodes and cloud resources based on feature entropy. The model was trained and validated on the MIMIC-CXR (v2.0.0) ( n = 377, 110) and CheXpert ( n = 224, 316) datasets, employing a 70/10/20 split. Results The proposed orchestration framework achieved an AUC-ROC of 0.962 [95% CI: 0.941–0.983] for Atelectasis detection, comparable to full-cloud inference, while reducing network bandwidth consumption by 64.3%. McNemar’s test indicated no statistically significant difference in diagnostic accuracy between the orchestrated hybrid approach and the full-precision cloud baseline ( p > 0.05), despite a 120 ms reduction in mean inference latency. Clinical Significance By embedding clinical feature extraction directly into the network orchestration logic, this framework enables real-time, zero-touch provisioning of diagnostic resources, facilitating reliable AI deployment in rural and bandwidth-limited clinical settings.

<p>The sixth generation of wireless networks (6G) will require network automation to meet the rapidly increasing demands for high data rate services, ultra-low latency, massive connectivity, and seamless integration with emerging technologies, while effectively reducing operating costs. To address these demands, the concept of Zero-Touch Networks (ZTNs) has been proposed, where Artificial Intelligence (AI) and Machine Learning (ML) play crucial roles in optimizing network performance, enabling intelligent decision-making, and ensuring efficient resource allocation. However, the implementation of ZTNs is subject to security challenges that may hinder their development and deployment. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of specific attacks targeting AI/ML models. In this survey paper, a comprehensive review of the security vulnerabilities and issues with ZTNs is conducted. Additionally, potential automated solutions to ZTN security concerns, with a specific focus on leveraging Automated ML (AutoML) technologies, are investigated. Two case studies are conducted to address security issues in ZTNs and further corroborate our findings: the development of autonomous intrusion detection systems and the creation of defense mechanisms against Adversarial ML (AML) attacks. Finally, some of the challenges and future research directions for the development of ZTN security approaches are discussed.</p>

Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational efficiency, support intelligent decision-making, and ensure effective resource allocation. However, the implementation of ZTNs is subject to security challenges that need to be resolved to achieve their full potential. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models. In this survey paper, we provide a comprehensive review of current security issues in ZTNs, emphasizing the need for advanced AI/ML-based security mechanisms that require minimal human intervention and protect AI/ML models themselves. Furthermore, we explore the potential of Automated ML (AutoML) technologies in developing robust security solutions for ZTNs. Through case studies, we illustrate practical approaches to securing ZTNs against both conventional and AI/ML-specific threats, including the development of autonomous intrusion detection systems and strategies to combat Adversarial ML (AML) attacks. The paper concludes with a discussion of the future research directions for the development of ZTN security approaches. Abstract content goes here