arXiv subjects

Leyli Karaçay

Publications and source records attributed to Leyli Karaçay.

3 recordsLinked to original sources

arXiv2026

Trustworthy, Explainable, and Sustainable Decentralized Intelligence for 6G Networks

As 6G networks transition from theoretical frameworks into operational realities, artificial intelligence (AI) evolves from an add-on optimization tool into a distributed and interconnected structural layer. Unlike previous network generations that mostly relied on centralized cloud analytics platforms, AI-native 6G networks operate across a dynamic, multi-domain edge-cloud continuum where data originates from heterogeneous sources including user devices, radio access networks, sensing infrastructures, and vertical applications. Centralizing this massive volume of data creates severe communication overhead, unacceptable latency bottlenecks, single points of failure, and complex cross-domain governance challenges. Consequently, decentralization becomes a fundamental architectural requirement for future 6G network intelligence and zero-touch operations. Security serves as the primary enabler of this decentralized paradigm. Critical security functions, such as real-time threat detection, physical-layer attack mitigation, slice protection, and intrusion detection, require immediate access to local context and telemetry before operational data loses its value. However, moving intelligence to the edge via collaborative paradigms like federated learning (FL) and decentralized FL (DFL) introduces complex trade-offs. System security cannot be addressed in isolation; it is deeply intertwined with equally important aspects like trustworthiness, explainability, and energy sustainability. Taking these aspects into account, this paper develops a unified perspective on decentralized intelligence for 6G, arguing that decentralization, trustworthiness, explainability, and sustainability must be designed jointly rather than treated as independent requirements

cs.NI↗

arXiv2025

Authorization of Knowledge-base Agents in an Intent-based Management Function

As networks move toward the next-generation 6G, Intent-based Management (IbM) systems are increasingly adopted to simplify and automate network management by translating high-level intents into low-level configurations. Within these systems, agents play a critical role in monitoring current state of the network, gathering data, and enforcing actions across the network to fulfill the intent. However, ensuring secure and fine-grained authorization of agents remains a significant challenge, especially in dynamic and multi-tenant environments. Traditional models such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC) and Relational-Based Access Control (RelBAC) often lack the flexibility to accommodate the evolving context and granularity required by intentbased operations. In this paper, we propose an enhanced authorization framework that integrates contextual and functional attributes with agent roles to achieve dynamic, policy-driven access control. By analyzing agent functionalities, our approach ensures that agents are granted only the minimal necessary privileges towards knowledge graphs.

cs.CR↗

arXiv2023

Practical Adversarial Attacks Against AI-Driven Power Allocation in a Distributed MIMO Network

In distributed multiple-input multiple-output (D-MIMO) networks, power control is crucial to optimize the spectral efficiencies of users and max-min fairness (MMF) power control is a commonly used strategy as it satisfies uniform quality-of-service to all users. The optimal solution of MMF power control requires high complexity operations and hence deep neural network based artificial intelligence (AI) solutions are proposed to decrease the complexity. Although quite accurate models can be achieved by using AI, these models have some intrinsic vulnerabilities against adversarial attacks where carefully crafted perturbations are applied to the input of the AI model. 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 by applying a successful adversarial sample, even in the most constrained circumstances. We also demonstrate that the risk associated with these kinds of adversarial attacks is higher than the conventional attack threats. Detailed simulations reveal the effectiveness of adversarial attacks and the necessity of smart defense techniques.

eess.SP↗