arXiv subjects

Chafika Benzaïd

Publications and source records attributed to Chafika Benzaïd.

3 recordsLinked to original sources

arXiv2026

Enhancing SDVN Performance via Policy-Driven Lightweight Control-Plane Resizing Strategies

Software-defined vehicular networks (SDVNs) under high mobility and fluctuating traffic demand offer programmable, centralized control for latency-sensitive intelligent transportation systems. However, data-plane Quality of Service (QoS) is often degraded by control-plane overload due to frequent handovers and dense vehicle-to-infrastructure (V2I) contacts. To address this, we propose two lightweight mechanisms for low-latency control-plane resizing in multi-controller SDVNs. The first - \textit{Control-plane Centric Control-plane Resizing Mechanism} - proactively offloads roadside units from overloaded controllers to underloaded or idle ones when a predefined load threshold is exceeded, preventing prolonged overload with minimal decision latency. The second - \textit{Data-plane Centric Control-plane Resizing Mechanism} - triggers resizing based on observable data-plane QoS degradation, such as average round-trip time exceeding a QoS threshold, aligning control-plane adaptation with V2I service experience. Both mechanisms are implemented and evaluated on Mininet-WiFi emulation testbeds with realistic worst-case vehicles mobility. Compared to fixed single-controller and static multi-controller benchmarks, the proposed algorithms significantly reduce end-to-end delay and packet loss while improving load balancing rate.

cs.NI↗

arXiv2026

Towards Securing IIoT: An Innovative Privacy-Preserving Anomaly Detector Based on Federated Learning

In the light of the growing connectivity and sensitivity of industrial data, cyberattacks and data breaches are becoming more common in the Industrial Internet of Things (IIoT). To cope with such threats, this study presents an anomaly detection system based on a novel Federated Learning (FL) framework. This system detects anomalies such as cyberattacks and protects industrial data privacy by processing data locally and training anomaly detection models on industrial agents without sharing raw data. The proposed FL framework incorporates two key components to enhance both privacy and efficiency. The first component is Homomorphic Encryption (HE), which is integrated into the framework to further protect sensitive data transmissions such as model parameters. HE enhances privacy in FL by preventing adversaries from inferring private industrial data through attacks, such as model inversion attacks. The second component is an innovative dynamic agent selection scheme, wherein a selection threshold is calculated based on agent delays and data size. The purpose of this new scheme is to mitigate the straggler effect and the communication bottleneck that occur in traditional FL architectures, such as synchronous and asynchronous architectures. It ensures that agents are not unfairly selected by the different delays resulting from heterogeneous data in IIoT environments, while simultaneously improving model performance and convergence speed. The proposed framework exhibits superior performance over baseline approaches in terms of accuracy, precision, F1-scores, communication costs, convergence speeds, and fairness rate.

cs.CR↗

arXiv2023

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

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.

cs.CR↗