warm systems have attracted growing interest due to Internet of Things (IoT) advancements and their applications in multi-robot control, environmental mapping, and intelligent transportation. Average consensus protocols are crucial in these scenarios, but current implementations require frequent interagent data exchange, posing significant risks to network security. warm systems have attracted growing interest due to Internet of Things (IoT) advancements and their applications in multi-robot control, environmental mapping, and intelligent transportation. Average consensus protocols are crucial in these scenarios, but current implementations require frequent interagent data exchange, posing significant risks to network security. In this paper, we present a privacy-preserving consensus algorithm designed to ensure the privacy of the initial state while achieving consensus on the precise average of the initial values, even in the presence of denial-of-service (DoS) attacks. First, to protect agent state privacy, we introduce perturbation to the transmitted information among the agents using independently generated random noises drawn from Laplace distributions, in conjunction with the utilization of specifically designed pseudo-random values. Then, by synthesizing the perturbed average consensus algorithm with an event-triggered control strategy, a distributed privacy-preserving consensus protocol is established for the swarm systems under DoS attacks. The presented results indicate that under the given network communication conditions, the event-triggered mechanism can effectively withstand the impact of DoS attacks on the network and ensure the achievement of accurate average consensus. Finally, experimental results on a UAV-swarm board based Wi-Fi network are provided to validate the effectiveness of obtained theoretical results.

This article is concerned with the co-design of privacy-preserving and resilient consensus protocol for a class of multiagent networks (MANs), where the information exchanges over communication networks among the agents suffer from eavesdropping and Sybil attacks. First, we introduce a new attack model in which an adversarial agent could launch a Sybil attack, generating a large number of spurious entities in the network, thereby gaining disproportionate influence. In this communication framework, a whole-process privacy-preserving mechanism is designed that is capable of protecting both initial and current states of agents. Then, instead of existing methods requiring identifying and mitigating Sybil nodes, a degree-based mean-subsequence-reduced (D-MSR) resilient strategy is implemented, showcasing its significant properties: 1) ensuring the effectiveness of aforementioned designed privacy protection strategy; 2) allowing the network to contain Sybil nodes without elimination; and 3) reaching consensus among the normal agents. Finally, several numerical simulations are provided to validate the effectiveness of the proposed results.

This paper investigates the consensus problem of multi-agent systems in heterogeneous networks against Byzantine attacks. Heterogeneous networks consist of different subsystems, and existing resilient consensus controllers for heterogeneous networks require specifying specific protocols for each subsystem, with no possibility for interaction between agents in different subsystems. In this study, we present a novel framework, based on hypergraph theory, for representing the complex connections within heterogeneous networks. These networks consist of diverse subsystems, including both discrete and continuous components, and also involve diverse update strategies. Based on the network model, a unified distributed resilient consensus protocol based on Weighted-Mean-Subsequence-Reduced (W-MSR) algorithm is developed and network connectivity conditions are provided in the presence of Byzantine attacks. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm and support the main theoretical findings.

Change point detection in dynamic networks aims to detect the points of sudden change or abnormal events within the network. It has garnered substantial interest from researchers due to its potential to enhance the stability and reliability of real-world networks. Most change point detection methods are based on statistical characteristics and phased training, and some methods are required to set the percent of change points. Meanwhile, existing methods for change point detection suffer from two limitations. On one hand, they struggle to extract snapshot features that are crucial for accurate change point detection, thereby limiting their overall effectiveness. On the other hand, they are typically tailored for specific network types and lack the versatility to adapt to networks of varying scales. To solve these issues, we propose a novel unified end-to-end framework called Variational Graph Gaussian Mixture model (VGGM) for change point detection in dynamic networks. Specifically, VGGM combines Variational Graph Auto-Encoder (VGAE) and Gaussian Mixture Model (GMM) through joint training, incorporating a Mixture-of-Gaussians prior to model dynamic networks. This approach yields highly effective snapshot embeddings via VGAE and a dedicated readout function, while automating change point detection through GMM. The experimental results, conducted on both real-world and synthetic datasets, clearly demonstrate the superiority of our model in comparison to the current state-of-the-art methods for change point detection.

This article investigates the secure and privacy-preserving consensus problem of multiagent systems (MASs) with directed interaction topologies under multiple cyberattacks, which contain deception attacks and DoS attacks. First, a unified attack model is introduced to characterize such a multiple attack phenomenon. Besides, considering the existence of eavesdroppers who can intercept the data transmitted on the links, a fully distributed agent value reconstruction method based on the idea of state decomposition is designed to prevent the leakage of the agent's initial information. Then, a novel privacy-preserving adaptive resilient consensus algorithm (PPARCA) with certain graph robustness condition for MASs under the multiple cyberattacks is proposed. The algorithm adaptively takes different countermeasures in the face of different cyberattacks. PPARCA uses the reconstructed agents' states and combines with the modified secure acceptance and broadcast algorithm (SABA). Theoretical analysis shows that the proposed algorithm can effectively protect the privacy of the initial state of the agents, and reach resilient consensus in the face of cyberattacks. Finally, numerical simulations and Raspberry Pi MASs practical application experiments demonstrate the effectiveness of the proposed results.

Security of consensus control is of key significance in multi-agent systems. In this paper we investigate the resilient consensus problem for multi-agent systems under the specific attack scenarios where the attacker can eavesdrop on initial information of agents among the system, and modifies the values in the communication links to interfere in the consensus process. To protect the privacy of node' value, we employ the cryptography of homomorphic encryption to encrypt initial integer state of each agent, without revealing to other agents the real value in the network. When the communication network meets the necessary connectivity, we develop a variation of so-called ratio consensus algorithm that deal with malicious attacks. The numerical example of directed network is conducted to illustrate the effectiveness of our proposed algorithm.

The problem of authenticating a re-sampled image has been investigated over many years. Currently, however, little research proposes a statistical model-based test, resulting in that statistical performance of the resampling detector could not be completely analyzed. To fill the gap, we utilize a parametric model to expose the traces of resampling forgery, which is described with the distribution of residual noise. Afterward, we propose a statistical model describing the residual noise from a resampled image. Then, the detection problem is cast into the framework of hypothesis testing theory. By considering the image content with designing a texture weight map, two types of statistical detectors are established. In an ideal context in which all distribution parameters are perfectly known, the likelihood ratio test (LRT) is presented and its performance is theoretically established. An upper bound of the detection power can be successfully obtained from the statistical performance of an LRT. For practical use, when the distribution parameters are not known, a generalized LRT with three different maps based on estimation of parameters is established. Numerical results on simulated data and real natural images highlight the relevance of our proposed approach.