Money laundering in Bitcoin networks threatens social stability and undermines global economic security, making its detection a top priority for governments and relevant sectors. However, existing detection methods often struggle to extract critical transaction information due to the inherent heterogeneity and massive noise in Bitcoin networks, resulting in low detection performance. To address this challenge, we propose a novel suspicious subgraph-enhanced method to identify covert money laundering activities in Bitcoin networks. Our core innovation is a semantic-driven subgraph analysis strategy, realized as a proactive financial purification mechanism. This strategy fundamentally reframes the problem by focusing on semantic relevance over mere topology, thereby achieving active denoising and functional isolation. Crucially, it successfully mitigates the inherent feature dilution problem in Graph Neural Networks (GNNs) by providing a semantically pure, signal-rich subgraph input. Experiments on large-scale Bitcoin transaction data confirm the practical superiority of our approach, demonstrating a significant 20% increase in recall compared to traditional Graph Convolutional Network (GCN) models, with high overall accuracy (96%) and recall (98%), thus meeting stringent regulatory requirements.

While 5G networks have accelerated the development of the Internet of Things (IoT), they have also introduced a large number of vulnerable IoT devices into the network, which would lead to severe Distributed Denial-of-Service (DDoS) attacks. The newly emerging DDoS attack methods generally have a shorter duration, which imposes higher requirements for the response time of DDoS mitigation technologies. Existing DDoS defense methods cannot achieve real-time detection due to the difficulty of reducing the delay of feature extraction and large-scale data processing. In this article, we focus on the timeliness of DDoS detection and mitigation. We hope that deploying effective defense countermeasures at the source side will block the majority of DDoS attack traffic in real time before it enters the data network (DN). To this end, we propose a real-time DDoS defense framework based on multidomain collaboration that combines multisource information to detect attack sessions with high accuracy in 5G networks. To operate the framework at line rate, we propose an optimal packet sampling strategy based on the accurate session size estimation, which can greatly reduce the detection overhead while ensuring good accuracy. In a typical scenario with an attack session size larger than 10, this method can achieve a 99% detection rate while reducing the packet inspection rate (PIR) to less than 37%.

In the case of natural disasters such as floods and earthquakes, ground infrastructure is vulnerable to damage, so a large amount of IoT data cannot be processed in time. Therefore, aerial equipment such as UAV or satellite is needed to provide temporary emergency services. This paper considered a satellite/UAV-assisted computing and offloading networks with spectrum sharing, which can make full use of the deployment flexibility of UAVs, the broad coverage of satellite and the expansibility of unlicensed band. For the UAV and IoT devices, system energy consumption is critical, as well as ensuring each mission is handled timely. So we proposed a scheme to minimize the total energy consumed by the system for computation and transmission while satisfying the maximum tolerable total delay constraint. We jointly optimize UAV location, uploading decisions, computing resource allocation and frequency band selection, without decreasing the WiFi transmission rate in the unlicensed band. The simulation result shows that the proposed scheme can effectively reduce energy consumption, while guarantee the total delay.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Using a typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of the UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by using problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework.

We consider the physical layer security for the downlink system, where the confidential messages can be wiretapped by both the intended users (IUs) and the external eavesdroppers (EEs). Regularized channel inversion (RCI) precoding is adopted for multiuser communication because it can control the mutual information leakage among users. And, to enhance the secrecy performance, artificial noise (AN) is introduced to disturb external eavesdroppers. Considering the large-system regime, by using stochastic geometry, the secrecy outage probability (SOP) and mean secrecy rate for the nearest EE wiretap and the strongest EE wiretap scenarios are derived, respectively. Analytical and simulation results show that i) when the AN is adopted, the SOP exponentially decays with the number of transmission antennas, ii) in most of the power allocation case, per-user secrecy rate can be improved significantly, e.g., there is an almost 2.7 times of improvement for the particular transmission antennas number.

The recently emerged compressive sensing (CS) theory provides a whole new avenue for data gathering (DG) in wireless sensor networks (WSNs) with the benefits of energy efficient. The exiting CS based DG approaches design the routing processes based on the assumption that there is a transform that can sparsely represent the sensor readings. However, most of the real sensor readings are well approximated by the sparse signals, which increases the amount of the data traffic to obtain the exact reconstruction, specially in the noisy situation. In this paper, we present an adaptable CS based DG scheme in WSNs. The autoregressive (AR) model based optimization problem is considered for the successive reconstruction and the reduction of the data traffic, in which AR model and the routing processes are jointly introduced into the reconstruction of the sensor readings. The AR parameters are evaluated with the historical sensor readings at the sink, by taking advantage of the similar spatial correlation. Then, with the known AR parameters, the sensor readings are reconstructed by extending the BPDN algorithm to the AR model based optimization problem. Our experimental results indicate that the proposed DG scheme outperforms the exist random walk (RW) based DG scheme in terms of reconstruction error and energy efficient, which results in better reconstruction accuracy with the RWs with the significantly smaller length.

In mobile satellite service (MSS) system, channel using efficiency is still an important factor because the system's frequency reuse factor can't reach 1 exactly. Here we try to introduce channel borrowing technique into MSS system. In channel borrowing technique, if channels in adjacent cells are allocated, these channels can't be borrowed. Therefore, in high spectrum reuse scenario, this may result in insufficient borrowable channels because only when none of the adjacent cells is using this channel can this channel be borrowed. In this paper, we propose a channel reallocation scheme based on matching theory in graph theory to solve this problem by lowering the interference level. Further, we put forward a simplified channel reallocation scheme to reduce the computational complexity. As the simulation results show, our schemes can efficiently improve the system's performance.

Recently, the performance bounds of the compressed sensing (CS) recovery algorithms have been investigated in the noisy setting. However, most of the papers only focus on the noisy measurement model where the signal is noiseless and the noise enters after the CS operation. The noisy signal model where both the signal and the compressed measurements are contaminated by the different noises is not considered. This paper works on the noisy signal model and provides the performance bounds for the following popular recovery algorithms: thresholding and orthogonal matching pursuit (OMP), Dantzig selector (DS) and basis pursuit denoising (BPDN). The performance of the recovery algorithms is quantified as the ℓ2distance between the reconstructed signal and the true noisy signal. Next, the impacts of the noise are analyzed on the basis of the quantified performance. The analysis results show that the effective way to restrain the impact of the noise is to choose the measurement matrix with low correlation between the columns or the rows. Finally, the theoretical bounds are verified with numerical simulations by calculating the mean-squared-error for the different noise variances. The simulation results show that OMP owns the better performance than the other three recovery algorithms under the noisy signal model.