Colorectal cancer (CRC) is a highly heterogeneous disease with distinct molecular subtypes, exhibiting unique transcriptional programs and clinical behaviors. The development of subtype-specific gene regulatory network (GRN) prediction is critical for uncovering the dysregulated transcriptional circuits driving CRC progression, metastasis and therapy resistance. However, the existing GRN prediction methods have limitations in addressing data sparsity and generalization across CRC subtypes, failing to effectively capture subtype-specific regulatory relationships and struggling to handle uncharacterized regulatory factors. In this study, we propose a novel computational framework, Dual-view Contrastive Gene Regulatory Network (DiCoGRN), which integrate structural and semantic information through dual-view contrastive Transformer to infer CRC subtype-specific GRNs. DiCoGRN identifies different cell subpopulations (CRC subtypes) and applies Graph Contrastive Learning (GraphCL) to learn structural embeddings for each subtype. Then, DiCoGRN retrieves the highly variable genes and driver genes of CRC subtypes from the NCBI database, and uses a large language model (LLM) to extract the semantic embeddings of genes. These two-view information are fused through cross-attention and gating interaction mechanisms, ultimately inferring subtype-aware regulatory connections. Experimental results show that the proposed DiCoGRN outperforms existing methods in recovering regulatory interactions and generalization across 12 subtypes. The performed vitro wet experiments illustrate that GATA3 from our predicted regulatory relationships may be a potential CRC driver gene. Our method not only advances CRC subtype-specific GRN inference but also helps to advance toward personalized CRC treatment. Codes and data are available at https://github.com/Fraid-H/DiCoGRN.

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.

Convolutional neural networks (CNNs) and Transformer-based models have achieved remarkable success in hyperspectral image (HSI) classification tasks due to their outstanding ability to extract spatial and spectral features. However, most existing methods process spatial and spectral features separately, making it difficult to effectively learn their interactive features. To address this issue, we propose a spectral-spatial dual-branch fusion Transformer (S2DBFT) for HSI classification. Initially, we construct a spectral feature extraction module (SPEEM) and a spatial feature extraction module (SPAEM) to extract low-level features. These two modules consist of a one-dimensional convolution layer and a two-dimensional convolution layer, respectively, performing shallow extraction of spectral and spatial features. Next, the two feature sets obtained are fused through a weighted fusion process. Additionally, we design a multi-head spectral-spatial self-attention (MHS3A) mechanism to enhance the interactive fusion of spectral and spatial features. Upon completion of feature fusion, a linear layer is used to obtain the sample labels. Extensive experiments on four HSI datasets demonstrate the effectiveness of the proposed S2DBFT, compared to existing state-of-the-art methods. In terms of performance evaluation, the overall accuracy and average accuracy indicate the superiority and generalizability of S2DBFT.

As wind power systems expand in size and complexity, the imperative for cyber security measures becomes increasingly critical, especially in light of the escalating threat posed by the zero-dynamics attack. Unfortunately, when the relative degree of a continuous-time system is greater than two and the sampling period is small, the sampled-data system will inevitably introduce unstable zeros, making it vulnerable to attackers. Meanwhile, it is difficult for traditional output-based system monitoring methods to detect such attacks. The current mainstream approach employs the generalized hold or generalized sampler to achieve zero shifting, but the implementation is challenging. In this article, we propose a defense method based on electronically tunable passive components to address this challenge. The method effectively defends against the zero-dynamics attack by introducing a gain-scheduling combined with the real-time parameter adjustment function of the electronically tunable passive components to move the unstable zeros of the system. Simulation results show that this defense method can improve the ability of the wind power system to resist zero-dynamics attack.

The Controller Area Network (CAN) serves as a pivotal communication protocol for Electronic Control Units (ECUs) in modern automotive systems. However, the increasing interconnectivity and sophistication of these ECUs introduce significant vulnerabilities, rendering in-vehicle networks susceptible to a variety of cyber threats. This work presents a multi-class classification model for intrusion detection within vehicle CAN networks, utilizing Bidirectional Long Short-Term Memory (BILSTM) and Parc-Net architectures. We also introduce a novel feature extraction module that computes the rate of ID changes, significantly enhancing the model's detection capabilities. The proposed method was evaluated on the CAR-HACKING dataset, demonstrating remarkable performance. The proposed Intrusion Detection System (IDS) greatly contributes to the security of vehicle CAN networks by facilitating real-time detection and localization of potential intrusions.

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.

Multiagent reinforcement learning (MARL) promises outstanding performance for multiintersection traffic signal control systems (TSCS), enabling intelligent administration of cities. However, the vulnerability of MARL algorithms to adversarial attacks has raised concerns about the security of TSCS. In this article, we explore the robustness of MARL-based TSCS against adversarial attacks, propose a black-box multiobject attack strategy, and assign an attack budget to ensure stealthiness. We design a dynamic threshold-based selection of critical states to minimize the cumulative reward with a limited number of attacks. In addition, we present a lightweight agnostic dynamic threshold-based defense mechanism by enhancing the worst-case performance of the policy. We formulate it as a min-max optimization problem, i.e., minimizing the quantity of training sample alterations while maximizing the cumulative discount reward of policy against the perturbed states. Extensive experiments on simulation of urban mobility (SUMO) demonstrate that the proposed attack policy can significantly reduce the performance of TSCS.

Severe damage caused by transferable adversarial attacks has emerged as a prominent concern in recent years, especially in the smart grid. The security issue of the deep reinforcement learning (DRL)-based dynamic pricing system is directly related to the grid's reliability. Previous works have primarily focused on the attacks' transferability from the perspective of model architecture, whereas the concept of distribution bias offers a novel and relatively underexplored viewpoint. In this work, we propose transferable adversarial attacks with distribution (TAD) targeting the DRL model. The adversary emphasizes destroying the target model with the masqueraded malicious dataset while ensuring stealthiness. Concretely, the masqueraded dataset generated by the attacker is required to have a similar distribution to the original dataset, while perturb some of the critical samples to help the target model misdirect to the nonoptimal policy. To this end, we propose an innovative model named Masquerader, which leverages a variational auto-encoder and incorporates three elaborate loss functions to constrain the distribution and deviation of malicious samples. Extensive experiments in a DRL-based dynamic pricing system indicate that our attack strategy TAD could successfully perturb the target model's output. The aberrant flatness of retail prices and the grid system's reduction in daily profits further validate the attack's transferability and harmfulness.

In this paper, we address the issue of jamming attacks in the field of maritime communication and propose the application of reconfigurable intelligent surfaces (RISs) in anti-jamming communication at sea. The RIS is installed on an Unmanned surface vehicle (USV) to construct a RIS-assisted USV relay communication system, which mitigates jamming attacks while enhancing legitimate transmissions. Compared to traditional static RIS, the use of mobile USV with RIS enables better performance and greater flexibility. We jointly optimize the trajectory of the USV, the passive beamforming of the RIS, and the source power allocation for each time slot based on proximal policy optimization (PPO), aiming to maximize the average downlink throughput. Simulation results demonstrate that the deployment of RIS on USV effectively suppresses jamming attacks and protects legitimate transmissions.

With the huge advantages of iris in authentication and identification, research on protecting iris information in real-world applications has gradually become an important research topic. In this paper, a method to hide the real feature information of the iris is proposed. Such an algorithm aims to prevent iris information from being maliciously utilized otherwise there will be serious consequences. In particular, we focus on accurately manipulating the iris features and ensuring that this method can also prevent iris distortion, thus people could hardly pick out the difference between before and after modification with the naked eye. Furthermore, we verify and evaluate our method and its effect through similarity detection and feature matching. The results show that it has a good performance.

Similarity measure has long played a critical role and attracted great interest in various areas such as pattern recognition and machine perception. Nevertheless, there remains the issue of developing an efficient two-dimensional (2D) robust similarity measure method for images. Inspired by the properties of subspace, we develop an effective 2D image similarity measure technique, named transformation similarity measure (TSM), for robust face recognition. Specifically, the TSM method robustly determines the similarity between two well-aligned frontal facial images while weakening interference in the face recognition by linear transformation and singular value decomposition. We present the mathematical features and some odds to reveal the feasible and robust measure mechanism of TSM. The performance of the TSM method, combined with the nearest neighbor rule, is evaluated in face recognition under different challenges. Experimental results clearly show the advantages of the TSM method in terms of accuracy and robustness.

Model-free predictive control is a novel data-driven control approach. It can calculate directly the control input by using a great deal of input and output datasets. Moreover, compared with the conventional model predictive control, it does not need to construct a precise mathematical model. In this work, we propose the model-free predictive control to the motion control of the autonomous underwater vehicle(AUV). The nonlinear motion control problem of AUV is effectively solved and the proposed approach can enhance the performance of trajectory tracking for AUV. At last, we demonstrate the availability of this method by the numerical simulations of trajectory tracking.