This paper addresses the challenge of model mismatch in traditional time-of-arrival (TOA)-based localization methods for near-field or far-field source. We propose a unified TOA-based localization model that operates effectively in both scenarios. To estimate source direction, we introduce an algebraic closed-form solution, the two-step weighted least squares based on the modified polar representation (TSWLSMPR) method, which mitigates the issues associated with model mismatch. Additionally, we derive the Cramér-Rao Lower Bound (CRLB) as a benchmark to assess the performance of the proposed method. Simulation results demonstrate that the TSWLS-MPR method achieves the CRLB under varying levels of measurement noise, source distance, and source direction, confirming its accuracy and efficiency.

Fluid Antenna (FA) systems hold significant potential for enhancing physical layer security (PLS) by dynamically adjusting the positions of transmit antennas to suppress information leakage to eavesdroppers. However, the joint optimization of secure beamforming and FA positions is very challenging and remains unsolved, given the mutually coupled, non-convex and NP-hard nature of the problem. In this paper, we investigate the FA-assisted multi-user multiple-input single-output (MU-MISO) system for maximizing the downlink secrecy rate. First of all, we propose an alternating optimization (AO) framework to decouple the problem. For efficient FA position optimization, we introduce a low-sampling successive selection and successive convex approximation (L3S-SCA) method, which first selects a proper port in discrete space and subsequently refines the FA positions via continuous optimization. For secure beamforming, we reformulate the problem as an unconstrained optimization on Riemannian manifold, eliminating the errors from relaxing per-antenna power constraints (PAPC). We design the necessary Riemannian tools and propose a Limited-memory Riemannian Broyden-Fletcher-Goldfarb-Shanno (LRBFGS) method with low computational complexity. Comprehensive convergence and complexity analyses are conducted, and simulation results demonstrate the advantages of FA-assisted secure beamforming, as well as the superiority of our proposed algorithms in terms of performance and complexity.

In complex disaster relief or unmanned delivery scenarios, the collaboration of heterogeneous UAVs faces numerous difficulties with dynamic and harsh environments, such as limited resource capacity and communication constraints. Especially for task allocation, the computational and communication burden poses many challenges for real-time task assignment and execution. To achieve a high allocation efficiency, this paper presents a Two-Phase task allocation strategy within a hybrid architecture. In the centralized phase, considering UAVs’ task requirements and capability, an initial task allocation is made from an overall perspective. In this phase, it determines the suitable types and numbers of UAVs, which guarantees the completion of tasks and reduces the potential communication to the greatest extent. In the distributed phase, specific UAVs within type-specific clusters are selected for executing tasks considering individual capability and physical location. This reduces the risk of single-UAV failures, enhancing the robustness and scalability of task allocation. Additionally, during the distributed phase, an incremental update method is employed to reduce communication latency and resource consumption. Experimental analysis and comparisons with the existing approaches demonstrate that our approach effectively reduces communication overhead and significantly improves task allocation efficiency. Furthermore, in the event of UAV failure or malfunction, it allows for a swift reallocation of tasks to other available UAVs.

The computing power network(CPN), as a current research hotspot, aims to provide users with reliable, efficient, and secure computing capabilities. However, research on the routing algorithm for computing power network is limited. Additionally, existing computing power networks are mainly applied in general domains, lacking improvement methods tailored for special scenarios such as emergency communications, which are prone to vulnerabilities and fluctuations. In this paper, we propose a routing algorithm for computing power network, namely Graph Attention Q-Network(GAQN), which integrates GAT and DQN to optimize routing strategies with the objective of maximizing overall system throughput. To evaluate the performance of GAQN, we conduct comparative experiments with multiple baseline models across various network topologies. Additionally, we perform experiments on topologies with different numbers of node failures. The results demonstrate that the proposed algorithm outperforms the baseline algorithms overall and performs well in disrupted network topologies, proving its generalization and robustness.

Traditional vulnerability detection mostly ran on rules or source code similarity with manually defined vulnerability features. In fact, these vulnerability rules or features are difficult to be defined accurately, which usually cost much expert labor and perform weakly in practical applications. To mitigate this issue, researchers introduced neural networks to automatically extract features to improve the intelligence of vulnerability detection. Bidirectional Long Short-term Memory (Bi-LSTM) network has proved a success for software vulnerability detection. However, due to complex context information processing and iterative training mechanism, training cost is heavy for Bi-LSTM. To effectively improve the training efficiency, we proposed to use Extreme Learning Machine (ELM). The training process of ELM is noniterative, so the network training can converge quickly. As ELM usually shows weak precision performance because of its simple network structure, we introduce the kernel method. In the preprocessing of this framework, we introduce doc2vec for vector representation and multilevel symbolization for program symbolization. Experimental results show that doc2vec vector representation brings faster training and better generalizing performance than word2vec. ELM converges much quickly than Bi-LSTM, and the kernel method can effectively improve the precision of ELM while ensuring training efficiency.

A prominent security threat to unmanned aerial vehicle (UAV) is to capture it by GPS spoofing, in which the attacker manipulates the GPS signal of the UAV to capture it. This paper introduces an anti‐spoofing model to mitigate the impact of GPS spoofing attack on UAV mission security. In this model, linear regression (LR) is used to predict and model the optimal route of UAV to its destination. On this basis, a countermeasure mechanism is proposed to reduce the impact of GPS spoofing attack. Confrontation is based on the progressive detection mechanism of the model. In order to better ensure the flight security of UAV, the model provides more than one detection scheme for spoofing signal to improve the sensitivity of UAV to deception signal detection. For better proving the proposed LR anti‐spoofing model, a dynamic Stackelberg game is formulated to simulate the interaction between GPS spoofer and UAV. In particular, for GPS spoofer, it is worth mentioning that for the scenario that the UAV is cheated by GPS spoofing signal in the mission environment of the designated route is simulated in the experiment. In particular, UAV with the LR anti‐spoofing model, as the leader in this game, dynamically adjusts its response strategy according to the deception’s attack strategy when upon detection of GPS spoofer’s attack. The simulation results show that the method can effectively enhance the ability of UAV to resist GPS spoofing without increasing the hardware cost of the UAV and is easy to implement. Furthermore, we also try to use long short‐term memory (LSTM) network in the trajectory prediction module of the model. The experimental results show that the LR anti‐spoofing model proposed is far better than that of LSTM in terms of prediction accuracy.

Software vulnerabilities are usually caused by design flaws or implementation errors, which could be exploited to cause damage to the security of the system. At present, the most commonly used method for detecting software vulnerabilities is static analysis. Most of the related technologies work based on rules or code similarity (source code level) and rely on manually defined vulnerability features. However, these rules and vulnerability features are difficult to be defined and designed accurately, which makes static analysis face many challenges in practical applications. To alleviate this problem, some researchers have proposed to use neural networks that have the ability of automatic feature extraction to improve the intelligence of detection. However, there are many types of neural networks, and different data preprocessing methods will have a significant impact on model performance. It is a great challenge for engineers and researchers to choose a proper neural network and data preprocessing method for a given problem. To solve this problem, we have conducted extensive experiments to test the performance of the two most typical neural networks (i.e., Bi-LSTM and RVFL) with the two most classical data preprocessing methods (i.e., the vector representation and the program symbolization methods) on software vulnerability detection problems and obtained a series of interesting research conclusions, which can provide valuable guidelines for researchers and engineers. Specifically, we found that 1) the training speed of RVFL is always faster than Bi-LSTM, but the prediction accuracy of Bi-LSTM model is higher than RVFL; 2) using doc2vec for vector representation can make the model have faster training speed and generalization ability than using word2vec; and 3) multi-level symbolization is helpful to improve the precision of neural network models.