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.

With industrial control systems (ICSs) commonly connected to the cloud, the security of ICS has received widespread attention. Intrusion detection systems (IDSs) are widely employed to protect ICSs, yet most existing intrusion detection models require expert systems to select the important features and large amount of storage space to store data, both result in increased costs. This paper proposes a preprocessing approach based on hash functions, which does not require prior knowledge and saves a lot of storage space. First, we compute a hash code for each piece of original data with the hash function. Next, the hash codes are converted to decimal and normalised. Finally, a number between 0 and 1 is obtained as a feature for intrusion detection. In experiments, we combine our method with various machine learning algorithms, and extensive experimental results illustrate that our method combined with support vector machine (SVM) and k-nearest neighbor (K-NN) achieve good detection results.

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.