Industrial Internet of Things (IIoT) faces increasing security risks with wide applications. Compared with the Internet, the IIoT has a broader attack surface and unique structural characteristics, posing difficulties in directly transferring the previous vulnerability analysis techniques. This paper proposes a triple-stage vulnerability analysis framework (TS-VulA) for IIoT via attack graphs combining ModernBERT and multi-layer heterogeneous networks, which includes three stages. In the first stage, the SentenceBERT based on ModernBERT and IIoT disruption losses are combined to conduct a single-node vulnerability assessment from the aspects of likelihood and criticality of vulnerability exploitation. In the second stage, an IIoT device importance calculation based on multi-layer heterogeneous network theory is proposed, which can acquire the inherent relationships among various IIoT devices. Stage 3 extends attack graph rules for IIoT, then computes node priorities by integrating vulnerability assessment and device importance, which can guide the mitigation strategy. Extensive experiments demonstrate that the proposed vulnerability assessment method achieves an average accuracy and average precision of 87.86% and 87.33%, both exceeding the existing methods. Simulated case studies illustrate that the proposed TS-VulA outperforms the prevailing vulnerability analysis methods.

With the booming development of the underwater Internet of Things, underwater devices and data volume are explosively increasing. Thanks to the advantages of high efficiency and low complexity, the emerging rate splitting multiple access (RSMA) can be integrated with underwater optical wireless communication (UOWC), which can help improve resource utilization and system performance. However, the oceanic characteristics are distinctive and pose great challenges for RSMA-enabled UOWC networks. Precoding design is crucial for benefitting from RSMA-enabled systems, and thus, we explore the precoding optimization for RSMA-enabled UOWC networks over underwater turbulent channels. Specifically, the RSMA-enabled UOWC network is modeled, where a combined turbulent channel involving the effects of absorption, scattering, and underwater turbulence is considered, and a message transmission scheme with rate splitting design is analyzed. A precoding problem is formulated to maximize the ergodic sum rate (ESR) under the constraint of total transmitted power. Since the accurate expression of ESR is a non-closed form and intractable, we first propose a consecutive Fenton-Wilkinson (CFW)-based ergodic rate approximation approach to solve this formulation problem. Fenton-Wilkinson moment matching is consecutively employed to achieve a closed-form and low-complexity expression of the ergodic rate, as only Fenton-Wilkinson moment matching method can offer closed-form solutions for underlying parameters of the approximating log-normal distributions. For further solving the non-convex precoding problem, the successive convex approximation (SCA) approach is adopted, which is particularly applicable for resource-limited underwater environments. The effectiveness of the proposed CFW approach and precoding strategy is evaluated by extensive simulation results for various user deployments and different network loads.

Distributed Denial of Service (DDoS) attacks threaten cloud servers by flooding redundant requests, leading to system resource exhaustion and legitimate service shutdown. Existing DDoS attack mitigation mechanisms mainly rely on resource expansion, which may result in unexpected resource over-provisioning and accordingly increase cloud system costs. To effectively mitigate DDoS attacks without consuming extra resources, the main challenges lie in the compromisesbetween incoming requests and available cloud resources. This paper proposes a resource-aware DDoS attack mitigation framework named RAM, where the mechanism of feedback in control theory is employed to adaptively adjust the interaction between incoming requests and available cloud resources. Specifically, two indicators including request confidence level and maximum cloud workload are designed. In terms of these two indicators, the incoming requests will be classified using proportional-integral-derivative (PID) feedback control-based classification scheme with request determination adaptation. The incoming requests can be subsequently processed according to their confidence levels as well as the workload and available resources of cloud servers, which achieves an effective resource-aware mitigation of DDoS attacks. Extensive experiments have been conducted to verify the effectiveness of RAM, which demonstrate that the proposed RAM can improve the request classification performance and guarantee the quality of service.

The previous robust adaptive beamformer for sparse array enjoys performance improvement due to enhanced degrees-of-freedom (DOFs), while the polarization diversity of signals is not considered. The polarization diversity is an important factor that could be exploited to design a more functional beamformer. Specifically, in this paper, a cascaded sparse array (CSA) composed of diversely polarized antennas, which is polarization sensitive and has reduced mutual coupling, is proposed with a closed-form expression for the array geometry. Then, a polarimetric sparse reconstruction beamformer operating in the joint spatial and polarization domain is proposed for the CSA, and it is capable of suppressing the interferences using the information in the additional polarization domain with enhanced DOFs. As a result, it not only offers reduced costs but also improved functionality, demonstrating its potential value in future wireless communications. The polynomial rooting based joint direction-of-arrival and polarization estimation procedures are then given to support the power distribution estimation in a closed-form manner. Subsequently, the estimated steering vector of the desired signal and the reconstructed interference-plus-noise covariance matrix are combined to calculate the proposed beamformer. Numerical simulations are included to verify the potential advantages of the proposed CSA as well as the superior performance and robustness of the proposed beamformer.

This paper introduces the rate-splitting multiple access (RSMA) to underwater wireless optical communications (UWOC) and investigates the ergodic rate metric for the RSMA-based UWOC system over turbulence-induced fading channels. Specifically, the model of the RSMA applied to UWOC is established, where an aggregated channel with the combined effects of absorption, scattering, and oceanic turbulence is considered. To quantity the ergodic rate of the RSMA-based UWOC system, an approximation of the instantaneous signal to interference plus noise ratio (SINR) is derived using the Fenton-Wilkinson moment matching method. With the approximated SINR, this paper further presents a high-accurate approximation of the ergodic rate in terms of the scaled complementary error function and Tayler series. Numerical results are demonstrated to evaluate the ergodic rate of the RSMA-based UWOC system and to validate the excellent match between the derived approximated analytical expression of ergodic rate and the results obtained from the original integral expression and Monte Carlo simulations.

With the explosive proliferation of connected devices and mobile users in the Internet-of-things, multiple access techniques are urged to be developed for the next generation wireless communications. Recently, rate-splitting multiple access (RSMA) has been a promising communication technology that holds advantages of strong robustness, low complexity, and high spectral efficiency, which can be integrated with the indoor visible light communication (VLC) broadcast system to compensate for the shortcomings of limited modulation bandwidth of LEDs. However, the research on the RSMA-based VLC systems is still in its infancy and there exist various problems to be explored. To benefit from the RSMA technique, this paper investigates the energy efficiency optimizations for both single-cell and multi-cell RSMA-based VLC broadcast systems. Specifically, these two systems are modeled, where the VLC broadcast channel follows Lambertian radiation model, and the splitting design and successive interference cancellation of RSMA are employed to mitigate the multi-user interference. Especially for multi-cell networks, the zero-forcing approach is adopted to eliminate the inter-cell interference. To maximize the energy efficiency, the precoding and power allocation problems are formulated for single-cell and multi-cell networks while accommodating multiple constraints including dynamic operation ranges of LEDs, QoS requirements, and interference elimination. For solving these non-convex fractional problems, two pieces of successive convex approximation (SCA)-based algorithms are proposed, in which the variable transformation and linear approximation are adopted. Simulation results indicate that the proposed schemes can achieve superior energy efficiency with fast convergence for various network loads and user deployments.

Multiuser multirelay network is a potential scenario to fulfill the transmission requirements of various sources and high-volume traffic for the Internet of Underwater Things. To efficiently complete concurrent transmissions for multiple users, this article investigates the joint relay assignment and power allocation problem for multiuser multirelay networks based on the underwater optical wireless communication (UOWC). Specifically, the multiuser multirelay network for UOWC based on decode-and-forward relaying is modeled, where the absorption, scattering, solar radiation noise, and oceanic turbulence of UOWC are all considered. The joint optimization problem of relay assignment and power allocation is formulated as a mixed-integer programming problem, where the average outage probability is minimized with the constraint of total transmitted power. To solve this joint problem, an alternating optimization method is employed, which alternately optimizes the relay assignment and power allocation subproblems. The relay assignment subproblem is modeled as a weighted bipartite matching problem and solved by an improved Kuhn-Munkres algorithm, whereas the power allocation subproblem is proved to be quasiconvex and solved by an iterative bisection algorithm. The simulation results indicate that the proposed schemes significantly reduce the average outage probability with fast convergence.

Cooperative transmission is a promising technology to expand communication range and improve system performance for underwater optical wireless communications (UOWCs). To adequately exploit the benefits of cooperative UOWCs, the relay selection and power allocation problems require to be explored. This paper investigates the relay selection and power allocation issues for the cooperative UOWC in the presence of solar radiation noise. Specifically, the cooperative UOWC based on amplify-and-forward (AF) relaying is modeled, where the effects of absorption, scattering, and solar radiation noise are all considered. We design two optimization schemes that minimize the energy consumption and maximize the overall signal-to-noise ratio (SNR), respectively. Both schemes are proved to be strictly quasi-convex and solved by Levenberg-Marquardt (LM) algorithm. The effectiveness of the proposed schemes is evaluated in line-of-sight (LOS) links and non-line-of-sight (NLOS) links, and the main factor affecting the relay selection and power allocation are derived for both LOS links and NLOS links.