In this paper, a robust design of an intelligent reflecting surface (IRS) and simultaneous wireless information and power transmission (SWIPT) assisted full-duplex non-orthogonal multiple access (NOMA) transmission scheme is studied by considering the expected scenarios for sixth-generation (6G) and beyond wireless networks. In the proposed scheme, IRS, SWIPT, NOMA, and full-duplex transmission efficiently tackle the spectrum and energy constraints of Internet of Things devices in the ultra-massive machine-type communications scenario of 6G networks. Obtaining perfect channel state information (CSI) for IRS-assisted schemes is practically challenging due to the lack of active elements and the high system overhead required for individual IRS element channel estimation; therefore, this paper proposes a robust scheme that considers imperfect CSI. Accordingly, the worst-case uplink sum rate maximization problem is formulated by considering the downlink user’s quality of service requirement as a constraint. An alternating algorithm is proposed to solve this challenging problem using the S-Procedure, the penalty convex-concave procedure, the successive convex approximation technique, the Schur complement, Generalized Petersen’s Lemma, and some algebraic manipulations. The provided numerical studies show that the proposed design performs better than the benchmark schemes and also provides more practical results than the non-robust scheme.
5G networks introduce major security and management challenges due to virtualization, service-based control, edge distribution, and highly dynamic traffic conditions. Although many intrusion detection studies report strong laboratory performance, most remain weakly connected to network and systems management requirements such as lifecycle automation, orchestration aware deployment, telemetry-efficient operation, and practical integration with virtualized 5G infrastructures. This paper presents V5GIDS, an AI-driven intrusion detection framework designed for deployment within the ETSI NFV MANO ecosystem. V5GIDS models detector functions as Virtualized Network Functions (VNFs) and organizes monitoring, analysis, policy propagation, and model management within a closed-loop security orchestration process aligned with Zero-Touch Service Management principles. To reduce control-plane overhead and avoid raw-data centralization, the framework adopts federated learning as a distributed management strategy and trains the proposed detector using only 2–3
The effective use of the vast amount of data generated by Internet-of-Things relies on employing appropriate data-sharing mechanisms that meet complex requirements such as scalability, performance, and security. Consequently, numerous blockchain-based data-sharing systems have been proposed in the literature to address these challenges across various application domains. However, these systems often strictly define how data is transferred between providers and consumers, which can limit their flexibility and compatibility. To address this issue, we propose a platform for traceable data sources with a customizable delivery method. The proposed platform is based on the integration of the InterPlanetary File System and a novel Peer-to-Peer file sharing environment. Additionally, the platform allows users to define their data in abstract terms and encourages innovative methods for data sharing and offers monetization opportunities for users’ code. We also present a decentralized, blockchain-agnostic approach for monetizing data sources utilizing symmetric key encryption. The effectiveness of the proposed platform is showcased and assessed through two data-sharing scenarios. Furthermore, we incorporate a trust model into our platform to isolate malicious content and enhance user confidence and engagement with social networking services among online users. Additionally, the platform enables users to monetize both their data sources and delivery methods through different public blockchains. We tested the platform using two use cases, and preliminary results indicate strong potential for big data analytics and decision-making applications. The overhead of the proposed platform ranges from 0.6 and 1.6
The most popular 5G technique for multiple antennas is MIMO (Multiple Input Multiple Output), which improves connectivity, speeds, and user experiences. Non-orthogonal multiple access (NOMA) has been designed as a strategy for improving spectral efficiency while supporting some multiple access interference at receivers in MIMO. Although NOMA is an efficient technology in 5G, it is more susceptible to physical layer attacks compared to present-day technology in cellular systems. One of the main performance bottlenecks in NOMA is the Pilot Contamination Attack (PCA), which prevents users from reaching their full potential in uplink and downlink. The attacker initiates the attack during the channel training phase when he injects the same pilot signal as the authorized users to control the channel estimate result and affect the precoding process. Detecting PCA attacks becomes more challenging in NOMA technology since we can not differentiate between authorized and malicious pilot signals in the same block. In this paper, we present an efficient detection technique for PCA in NOMA by using various statistical measurements for normal traffic as a reference profile, and then Bayes’ theorem is applied to classify any incoming traffic as normal or pilot attack. The simulation results show that the proposed technique succeed to detect the pilot contamination attack with a detection rate of up to 99
The increasing sophistication of cyber attacks has rendered traditional static password-based authentication mechanisms inadequate for secure identity verification in modern information systems. Attack techniques such as social engineering, credential theft, and phishing continue to bypass conventional security controls, underscoring the need for adaptive, continuous identity verification mechanisms within Zero Trust architectures. A multi-layer identity authentication architecture is developed by integrating Multi-Factor Authentication (MFA) with the Analysis of Competing Hypotheses (ACH) to enable dynamic and robust access control. The architecture adopts a three-layer verification framework that progresses from basic identity validation to advanced behavioral analysis. It consists of an Identity Authentication Engine, an ACH Engine, and a Policy Engine, which collectively perform multidimensional behavioral assessment across five factors: login time patterns, device characteristics, geolocation, user operational behavior, and network environment attributes. By replacing static, one-time authentication with continuous identity assessment, the proposed framework enforces strict verification at initial access and maintains real-time monitoring throughout the entire user session. This approach enables the detection of anomalous behavior and potential threats even when valid credentials are compromised. The architecture is evaluated using simulated financial transaction scenarios. Experimental results show that the system achieves 94.3
