Information dissemination in dynamic social networks enables fast and frequent access to social news. Thereinto, the coexistence of public and private information creates a hybrid dissemination dynamics process in social networks. However, most existing information dissemination models treat the hybrid information in isolation and fail to consider their interactions through shared nodes and temporal dependencies. Thus, we propose a novel hybrid information dissemination (HID) model that explicitly captures the interconnected dissemination mechanisms of both public and private information within dynamic social networks. Additionally, considering heterogeneity among individuals, we further design a decision-making algorithm for the proposed HID model, aiming at maximizing individuals’ initiative. Furthermore, we derive equilibrium points and analyze their stability for the proposed HID model. Numerous experiments are conducted, and results show that the proposed HID model can effectively describe the dissemination process of hybrid information.

Multi-target tracking (MTT) in real-world environments often faces the challenge of outlier measurements, which severely degrades the performance of standard MTT algorithms. This paper integrates the maximum correntropy criterion (MCC) into the Gaussian mixture probability hypothesis density (GM-PHD) filter, an implicit data association and a highly efficient MTT algorithm. The MCC provides a localized similarity measure that is inherently resilient to impulsive outliers. We embed an iterative fixed-point measurement update for the GM-PHD filter, and an adaptive kernel size design strategy is also devised. The proposed MCC-GM-PHD filter effectively suppresses the influence of large measurement residuals, while maintaining a closed-form Gaussian mixture representation. The performance of the proposed MCC-GM-PHD filter is verified via simulations.

This paper proposes a novel type of random finite set (RFS), namely augmented RFS, to address the problem of resolvable group target tracking, which integrates the information of both the group attributes and the dynamic state of group targets into random finite sets. Specifically, we initially introduce an augmented random finite set framework, incorporating group labels and group cardinality to estimate both the trajectories and states of group targets. Then, a new multi-target filter based on the augmented RFS is proposed to achieve the process of group target tracking. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed filter in group target tracking scenarios.

The vulnerability of wireless communication links to eavesdropping poses significant challenges in securing AAV-assisted networks. To enhance security, reconfigurable intelligent surfaces (RIS) and artificial noise (AN) have emerged as promising technologies for mitigating eavesdropping by controlling wireless propagation environments and introducing interference against eavesdroppers. However, existing works have rarely combined transmitter beamforming, RIS, and AN integratedly considered, and leveraging their complementary characteristics for efficient security enhancement remains challenging. Additionally, optimizing such system security performance is complicated by the nonconvexity of secrecy rate maximization and the highly time-varying communication links caused by the mobility of AAVs and users. To address these challenges, we propose a secure communication framework that integrates RIS and AN transmission devices on AAVs. To solve the resulting nonconvex optimization problem, we develop a dual-mode framework based on twin delayed deep deterministic policy gradient (TD3), employing two subenvironments that interact independently before updating a global environment. Extensive simulations demonstrate that the proposed approach significantly enhances secrecy rate performance compared to other methods.

Resolvable group target tracking is of great challenge due to the complex motion interaction between group targets, which leads to tracking performance degradation. To solve this problem, a cardinality-balanced multi-target multi-Bernoulli filter based on the graph theory and leader-follower model is proposed. In the proposed filter, firstly, the group targets are divided into leaders and followers by mean of the leader-follower model. Furthermore, the graph theory is used to establish the state transition equations between those divided group targets. Lastly, the process of state prediction is given, and its corresponding implementation is derived by Gaussian mixture approximations. Simulation experiments verify the superiority and effectiveness of the proposed filter.

Numerous studies have demonstrated, in-depth, the vulnerability of the deep reinforcement learning (DRL) model’s elements (e.g., reward), which is a factor limiting the widespread deployment of DRL in some crucial domains, including intelligent traffic signal control (ITSC). While partial poisoning attacks with insidious rewards are enabled undetectable by directly employing regularization or cumulative reward restrictions, these constraints are somewhat 1-D and fail to consider the time dependence of DRL. Moreover, the adversary should avoid injecting undesirable perturbations when agents’ policies are unstable, namely, effectively maximizing the attacking strategy’s benefit. It is thus a challenge to perturb the DRL model stealthily with as few disruption steps or modifications to the original sample as possible while ensuring the attack’s efficiency. In this work, two black-box reward space attack strategies are introduced, where we encourage the adversary to learn a malicious adversarial policy actively. The first is the Multiconstraint Stealthy Time Attack which is updated with the penalties earned by attacking crucial moments, and restricted through action confidence and perturbations’ total number, to ensure attack times’ stealthiness. The second technique is the multiobjective stealthy modification attack which is modeled as a multiobjective optimization problem, and the adversary balance attack performance and stealthy modification with weighting factor$\omega $. Extensive simulation results evaluated in SUMO, involving comparison assessment and attack distribution, exhibit a dramatic increase in average travel time, implying that our attacks impose pressure on the traffic flow, namely, the efficacy of proposed attack strategies.

The problem of multiple integrity attacks (attackers) detection and identification (MIADI) in cyber–physical systems (CPSs) is still a challenging problem to date. The goal of this article is to develop a knowledge-based method capable of simultaneously detecting and identifying multiple integrity attacks aiming at different sensors in a CPS. In this article, with the help of labeled random finite set (RFS) theory, a new solution to solve the MIADI problem is proposed. The main contributions of this article lie in the following two aspects, the first is the novel formulation of the MIADI problem, in which labeled RFSs are used to model the behaviors of multiple integrity attackers for the first time, and the second is the proposed labeled RFS-based solution, which provides an elegant framework to cope with the MIADI problem. Numerical experiments are conducted and experimental results demonstrate the effectiveness of the proposed solution. This proposed solution further extends the feasibility of the labeled RFS theory in the context of CPSs cybersecurity.

To solve the problem of range-spread targets detection in non-Gaussian clutter for dual-polarimetric radar systems, a novel dual-polarimetric persymmetric detector based on the two-step generalized likelihood ratio test (GLRT) is proposed. Firstly, the target signal is modeled as a multi-rank subspace signal, and the clutter is modeled as a compound Gaussian model with deterministic unknown texture. Then, by taking polarimetric and persymmetric characteristics of the clutter into account, the detection performance of the proposed detector is obviously improved. Finally, through the verification via both simulated data and measured data, it is shown that the proposed detector poses better detection performance in comparison with the existing non-polarimetric detectors. Due to the complexity of the detector form, the analytical expression of false alarm probability cannot be derived. Therefore, we verified the constant false alarm ratio (CFAR) characteristic of the proposed detector through simulation experiment.

This article is concerned with the problem of multiple attacks detection (MAD) for distributed sensor networks (SNs) under multiple malicious attacks. The goal of this article is to develop an effective method capable of simultaneously detecting multiple attacks in distributed SNs. By integrating the theories of random finite set (RFS), fusion rules, and consensus, a novel distributed filter named consensus arithmetic average Gaussian mixture probability hypothesis density (AA-GMPHD) filter is proposed in this article, which can achieve the simultaneous detection of multiple attacks in the context of distributed SNs. The main contribution of this article, lies in the proposed consensus AA-GMPHD filter that solves the MAD problem in distributed SNs for the first time. Simulation experiments confirm the effectiveness of the proposed filter for the distributed MAD problem in the context of distributed SNs.

In this paper, we propose a residual-based Global Position Stystem (GPS) spoofing detection and positioning recovery algorithm that uses inertial navigation system (INS) measurements to detect spoofing attacks on GPS receivers. We first calculate the error vector of satellite pseudorange, and use cumulative statistics to determine the validity of satellite data. Then, we utilize the remaining accurate GPS data and inertial measurement unit (IMU) data to achieve positioning recovery. Furthermore, we perform simulation experiments for fixed pseudorange bias and incremental pseudorange bias attacks, the results show that GPS spoofing is easily detected by using the proposed algorithm, which validates the effectiveness of the proposed algorithm.

Nowadays, accurate and real-time vehicle tracking is critical to ensure the safety of intelligent vehicles. However, tracking in the complex traffic environments still remains a challenging issue. In this article, we present a road-map aided Gaussian mixture probability hypothesis density (RA-GMPHD) filter for multivehicle tracking with automotive radar. Since the road-map is commonly available in traffic scenarios, we focus on leveraging road-map information to enhance the tracking performance. We first model the vehicle dynamics in a 2-D road coordinates, then approximatively map it onto ground coordinates considering map errors. Additionally, we integrate the variable structure interacting multiple model into the RA-GMPHD filter considering both the dynamic uncertainty of targets and the road geographic constraints. Furthermore, we perform extensive simulations and conduct physical testings to demonstrate the superiority of our approaches compared with state-of-the-art method. Experimental results show our methods enhance both the tracking quality and tracking continuity.

To invade a cyber-physical system (CPS) successfully, hackers are prone to simultaneously launching multiple cyber attacks on different sensors in a CPS. However, little attention has been paid to the problem of detecting multiple cyber attacks up to now. Therefore, in this paper, we deal with the problem on how to efficiently detect multiple cyber attacks aiming at different sensors in CPSs. To achieve the goal of simultaneously detecting both the number of attacks and the attacked sensors, we formulate this problem via a random finite set (RFS) theory, and then apply an iterative RFS-based Bayesian filter and its approximation to solve the problem. Four numerical experiments with different attacks are provided, and the results have demonstrated the effectiveness of the RFS-based approach for the problem of multiple attacks detection in CPSs.

Given the popularity of drones for leisure, commercial, and government (e.g., military) usage, there is increasing focus on drone regulation. For example, how can the city council or some government agency detect and track drones more efficiently and effectively, say, in a city, to ensure that the drones are not engaged in unauthorized activities? Therefore, in this article, we propose a crowdsensing-based cyber-physical system for drone surveillance. The proposed system, CSDrone, utilizes surveillance data captured and sent from citizens’ mobile devices (e.g., Android and iOS devices, as well as other image or video capturing devices) to facilitate jointly drone detection and tracking. Our system uses random finite set (RFS) theory and RFS-based Bayesian filter. We also evaluate CSDrone’s effectiveness in drone detection and tracking. The findings demonstrate that in comparison to existing drone surveillance systems, CSDrone has a lower cost, and is more flexible and scalable.

The wide proliferation of drones has posed great threats to personal privacy and public security, which makes it urgent to monitor and locate intruding drones in sensitive areas. In Direction of Arrival (DOA) based localization, the estimation accuracy of DOA directly affects the localization accuracy. In this paper, we propose a novel algorithm to estimate the DOA of an intruding drone by exploiting its acoustic feature, which is mainly reflected in the strength distribution of the harmonics of the received acoustic signal. Specifically, this algorithm first estimates the harmonic frequencies of the drone's acoustic signal in frequency domain. Then, multiple signal classification is used to estimate the DOAs of all the selected harmonics. Furthermore, weighted sum of these DOA estimates will be taken as the drone's DOA estimate, where the weights are in proportional to the energy of the corresponding harmonics. The performance of the proposed algorithm is verified by both simulation and field experiments.

Electronic countermeasure ECM attack has been an emerging threat to radar network in recent years. It is necessary to design a secured radar network against ECM attack. In this paper, we prove that the radar network with conventional measurement fusion schemes is insecure to deceptive ECM DECM attack. Then, a new measurement fusion scheme is proposed, which shows better security performance when DECM attack happens. Numerical simulations are presented to demonstrate the effectiveness of the proposed measurement fusion scheme. Copyright © 2016 John Wiley & Sons, Ltd.