Deep neural network (DNN)-based vehicular cameras, capable of detecting multiple road objects with high accuracy, enable critical autonomous driving applications such as lane changing and overtaking. However, their vulnerability to adversarial attacks exposes them susceptible to potential security risks. Recent studies indicate that adversarial patches can impair detector functionality by modifying vehicle perception attributes, such as color and distance. Currently, the design of effective adversarial patch attacks targeting vehicle bounding boxes has not been thoroughly investigated. Incorrect bounding boxes lead to the detector marking inaccurate vehicle locations, causing the vehicular system to make erroneous decisions (e.g., path deviation). In this paper, we propose BPATCH, an adversarial patch attack targeting the perception of vehicular bounding boxes. Our attack targets DNN-based camera detectors, leading to inaccurate recognition of both the size and location of the bounding box. We design a two-stage training process, where a random patch is first transformed and applied based on various factors for robustness, and then the output is used to optimize meaningful vehicular stickers for stealthiness in practical applications. We in particular introduce new loss functions to enhance the effectiveness of the attack. Extensive experiments on two datasets and the real-world application demonstrate the effectiveness of our method across various DNN-based models and stickers.

This paper proposes an improved vector current control (IVCC) strategy to integrate voltage source converter (VSC) based high voltage direct current (HVDC) to a very weak grid. In comparison to a constant value as used in the outer control loop in classical vector current control (VCC) strategy, the control references are adjusted according to the dynamic changes of system frequency. A small signal model including the system and the proposed control strategy is firstly developed. Based on the eigenvalue locus and the damping ratio analysis, the impact of adjusting different control loops is then investigated. Furthermore, an optimization model is built to coordinate the outer voltage control loop and the outer active power control loop of IVCC such that a wider range of phase-locked loop (PLL) gains can be applied, while reduced deviations of frequency and point of common coupling (PCC) voltage as well as a good enough fault-ride through capability can be achieved simultaneously. Finally, the effectiveness of applying IVCC in connecting VSC to a very weak grid is validated by detailed time domain simulations.

Generative models have demonstrated remarkable capabilities in synthesizing realistic content, creating new opportunities for secure communication through steganography---the practice of embedding covert messages within seemingly innocuous data. While prefix-based steganography, which encodes secret messages into shared probability intervals during generative sampling, has emerged as a promising paradigm for provably secure communication, its practical adoption remains constrained by inherent tradeoffs between security, capacity, and efficiency. To address these challenges, we propose two enhancements. The first enhancement optimizes quantization distortion in existing frameworks to minimize KL divergence, thereby enhancing theoretical security. The second redesigns the sampling mechanism via distribution coupling to amplify steganographic capacity, achieving this without incurring substantial computational overhead. Experimental validation on text generation task confirms our enhancements substantially outperform previous implementations, demonstrating notable capacity improvements, marked security enhancements, and efficiency gains on consumer-grade hardware. Cross-task comparisons with popular provably secure steganography further establish the proposed enhancements as achieving superior security-capacity-efficiency tradeoffs across diverse generative scenarios, advancing the practical deployment of provably secure steganography systems.

In this paper, we design a system to generate Pseudo Ground Control Points (PGCPs) using standard low-cost widely available GNSS receivers in a crowd-sourcing manner. We propose a number of GNSS points filters that removes different causes of errors and biases, and design a linear regression height estimator leading to high-accuracy PGCP elevations. Evaluation of our system shows that the PGCPs can achieve a median accuracy of 22.5 cm in 25 metropolitan areas in the USA.

LiDAR significantly contributes to autonomous driving systems (ADSs) through its perception, prediction and decision layers. Recent research has focused on implementing adversarial attack on LiDAR-based ADS by generating perturbed point cloud adversarial samples. However, most state-of-the-art attacks focus on stationary scenarios, making it difficult to apply them in dynamic scenarios with multiframe point clouds. In this article, we introduce SpotAttack, a novel adversarial attack that targets specific areas of a vehicle’s surface using distributed patches. Unlike traditional adversarial mechanism that mislead the object classification through pixel perturbations, our designed spots decrease the reflectivity of LiDAR rays, causing the point clouds in these patches to be obscured. As a result, the 3-D object detection network will produce incorrect pose estimations based on the adversarial point cloud samples. To ensure the effectiveness of SpotAttack in dynamic scenarios, we establish a position matrix for multiobjective optimization and adopt genetic algorithm (GA) to address the nondifferentiable issue in spots generation. We conduct extensive experiments in three typical scenarios, and the results demonstrate that the proposed attack can manipulate LiDAR perception and influence ADS decision making.

Federated learning (FL) is a promising paradigm for collaboratively training networks on distributed clients while retaining data locally. Recent work has shown that personal data can be recovered even though clients only send gradients to the server. To against the gradient leakage issue, differential privacy (DP)-based solutions are proposed to protect data privacy by adding noise to the gradient before sending it to the server. However, the introduced noise affects the training efficiency of local clients, resulting in low model accuracy. Moreover, the identity privacy of clients has not been seriously considered in FL. In this article, we propose FL2DP, a privacy-preserving scheme focusing on protecting the data privacy as well as the identity privacy of clients. Different from the current schemes that add noise sampled from the Gaussian or Laplace distribution, in our scheme the noise is added to the gradient based on the exponential mechanism to achieve high training efficiency. Then, clients upload the perturbed gradients to a shuffler, which reassigns these gradients with different identities. We give a formal privacy definition called gradient indistinguishability to provide strict unlinkability for gradients shuffle. We propose a new gradient shuffling mechanism by adapting the DP-based exponential mechanism to satisfy gradient indistinguishability using the designed utility function. In this case, an attacker cannot infer the real identity of the client via the shuffled gradient. We conduct extensive experiments on two real-world datasets, and the results demonstrate the effectiveness of the proposed scheme.

The emergence of vehicular applications such as collision warning enhances traffic efficiency and the driving experience of users. Due to the features of decentralization, complexity, and heterogeneity in intelligent transportation systems, the key issue is ensuring the legality and trustworthiness of all participating parties. Traditional methodologies such as public key infrastructure provide services for verifying the legitimacy of entities with certificates. However, even if a vehicle is legally registered, trust in its behavior is not always assured. Recently decentralized trust management systems (DTMS) are proposed to solve the trust issue. However, achieving both dependability and efficiency remains a challenge. In this paper, we propose a novel DTMS based on the consortium blockchain. We in particular focus on the dependability of trust evaluation, where trust computation is performed on both vehicles and RSUs. To achieve a sustainable trust environment, we propose an incentive model whereby the raters can earn rewards for providing honest ratings. Instead of utilizing the consensus algorithm based on the Proof of Work (PoW) or Proof of Stake (PoS), we implement a verifiable delay function (VDF)-based consensus model within the trusted execution environment (TEE) to ensure efficiency and security for the consortium blockchain. In addition, we design a smart contract on top of the blockchain to assist the system in detecting a specific attack against the trust system, known as the on-off attack. Through extensive experiments, the results of trust assessment and blockchain performance demonstrate the dependability and efficiency of our proposed DTMS.

The ability to accurately evaluate the performance of location determination systems is crucial for many applications. Typically, the performance of such systems is obtained by comparing ground truth locations with estimated locations. However, these ground truth locations are usually obtained by clicking on a map or using other worldwide available technologies like GPS. This introduces ground truth errors that are due to the marking process, map distortions, or inherent GPS inaccuracy.In this paper, we present a theoretical framework for analyzing the effect of ground truth errors on the evaluation of localization systems. Based on that, we design two algorithms for computing the real algorithmic error from the validation error and marking/map ground truth errors, respectively. We further establish bounds on different performance metrics.Validation of our theoretical assumptions and analysis using real data collected in a typical environment shows the ability of our theoretical framework to correct the estimated error of a localization algorithm in the presence of ground truth errors. Specifically, our marking error algorithm matches the real error CDF within 4%, and our map error algorithm provides a more accurate estimate of the median/tail error by 150%/72% when the map is shifted by 6m.

Connected vehicles will be a prominent feature of future Intelligent Transport Systems. Which means that there will be a very high volume of wireless traffic that vehicles will receive and process. Due to this large quantity of traffic, there will be Quality of Service (QoS) constraints on the system that means messages will need to be prioritised. As vehicles will have a finite buffer to hold messages, the prioritisation scheme must consider network throughput to ensure QoS requirements are met. In our throughput authentication prioritisation technique, a Markov model is used to detect abnormally large data traffic users who are potential attackers performing a Denial of Service (DoS). Our results show that the algorithm can efficiently enhance network throughput.

Summary Source location privacy (SLP) is an important property for a large class of security‐critical wireless sensor network (WSN) applications such as monitoring and tracking. In the seminal work on SLP, phantom routing was proposed as a viable approach to address SLP. However, recent work has shown some limitations of phantom routing such as poor data yield and low SLP. In this paper, we propose phantom walkabouts, a novel and more general version of phantom routing, which performs phantom routes of variable lengths. Through extensive simulations, we show that phantom walkabouts provides high SLP level than phantom routing under specific network configuration.

We examine the ability of networks formed by mobile sensor nodes to diffuse information in the case when communication is only possible during opportunistic encounters. Our setting assumes that mobile nodes are continuously sensing the world and acquiring new information. We form an abstract model of this situation and show by theoretical analysis, simulation, and real mobility data that the diffusion of information in this setting cannot be as efficient as when we allow arbitrary contact patterns between the nodes with the same overall contact statistics. This establishes a fundamental asymptotic limitation on the information diffusion capacity of such opportunistic mobile sensor networks - the encounter patterns arising out of physical motions in a geometric space are not ideal for information diffusion.

Source location privacy (SLP) is becoming an important property for a large class of security-critical wireless sensor network applications such as monitoring and tracking. Many routing protocols have been proposed that provide SLP, all of which provide a trade-off between SLP and energy. Experiments have been conducted to gauge the performance of the proposed protocols under different network parameters such as noise levels . As that there exists a plethora of protocols which contain a set of possibly conflicting performance attributes, it is difficult to select the SLP protocol that will provide the best trade-offs across them for a given application with specific requirements. In this paper, we propose a methodology where SLP protocols are first profiled to capture their performance under various protocol configurations. Then, we present a novel decision theoretic procedure for selecting the most appropriate SLP routing algorithm for the application and network under investigation. We show the viability of our approach through different case studies .

Providing an interface for network applications to access network state, Software-Defined Networking (SDN) northbound API protocol is the foundation for the development of programmable networks with adaptive applications. However, with the growing network scale and applications' need for routing state at multi-domain level, feeding complete routing states to applications would jeopardize their scalability and network providers' privacy. Thus a good routing state abstraction is needed, which must be on-demand so that different applications can receive customized abstract state suiting their needs. Moreover, it must be minimal and equivalent, i.e., containing all the necessary information for applications to make decisions as the complete state does with no redundancy. Current routing state abstractions are not on-demand, and adopt extreme aggregation approaches (e.g., the big switch) to provide a minimal abstraction with the price of severe information loss. For instance, bottleneck links shared between flows are concealed, leading applications to make sub-optimal decisions. In this paper, we design ORSAP, the first on-demand routing state abstraction protocol, through which network applications can describe their demands while Internet service providers can provide the on-demand minimal equivalent routing state accordingly. ORSAP ensures applications' scalability, protects network providers' privacy, and significantly reduces the traffic to disseminate the information. Experiments show that with ORSAP and the abstraction engine we introduced in this paper, one can achieve a state abstraction ratio of up to 60% with an extremely low computation time even with large networks and complex application queries.

Handling state dependencies is a major challenge in modern SDN programming, but existing frameworks do not provide sufficient abstractions nor tools to address this challenge. In this paper, we propose a novel, high-level programming abstraction and implement the *Function Automation SysTem (FAST)*. With the two key features, i.e., *automated state dependency tracking* and *efficient re-execution scheduling*, we demonstrate that FAST substantially simplifies state-dependent SDN programming and boosts the performance.

As wireless sensor networks (WSNs) have been applied across a spectrum of application domains, the problem of source location privacy (SLP) has emerged as a significant issue, particularly in safety-critical situations. In seminal work on SLP, phantom routing was proposed as an approach to addressing the issue. However, results presented in support of phantom routing have not included considerations for practical network configurations, omitting simulations and analyses with larger network sizes. This paper addresses this shortcoming by conducting an in-depth investigation of phantom routing under various network configurations. The results presented demonstrate that previous work in phantom routing does not generalise well to different network configurations. Specifically, under certain configurations, it is shown that the afforded SLP is reduced by a factor of up to 75.

Noncontact techniques for detecting vital signs have attracted great interest due to the benefits shown in medical monitoring and military applications. A rapid remote evaluation on physiological signal frequencies is needed in search and rescue operations as well as intensive care. However, the presence of respiration harmonics causes aliasing problems to heart-rate estimation, especially when the data volume is limited. By taking advantage of the simple pattern of physiological signals, we propose a stepwise atomic norm minimization method (StANM) to accurately assess the respiration and heartbeat frequencies with a limited data volume. First, the respiration frequency is estimated by the conventional atomic norm minimization. Then the frequencies of respiration harmonics are generated based on the inherent relationship between the fundamental tone and the harmonics. Finally, with the pre-estimated frequencies, we locate the heartbeat frequency by solving a modified atomic norm minimization problem. Simulations and experiments show that the proposed method can accurately estimate physiological frequencies from 6.5-second-long raw data with a 4-Hz sampling rate.

BACKGROUND: Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange. METHODS: We present a solvent signature that compactly summarizes the solvent distribution in the high-dimensional data, and then define a distance metric between different configurations using this signature. We next incorporate the solvent information into the construction of Markov state models and present a fast geometric clustering algorithm which combines both the solute-based and solvent-based distances. RESULTS: We have tested our method on several different molecular dynamical systems, including alanine dipeptide, carbon nanotube, and benzene rings. With the new solvent-based signatures, we are able to identify different solvent distributions near the solute. Furthermore, when the solute has a concave shape, we can also capture the water number inside the solute structure. Finally we have compared the performances of different Markov state models. The experiment results show that our approach improves the existing methods both in the computational running time and the metastability. CONCLUSIONS: In this paper we have initiated an study to build Markov state models for molecular dynamical systems with solvent degrees of freedom. The methods we described should also be broadly applicable to a wide range of biomolecular simulation analyses.

Connection reliability is one important service level agreement (SLA) parameter for a customer and should be carefully considered in survivable WDM networks. A sound scheme should guarantee customers' reliability and simultaneously benefit a service provider in resource efficiency. Under the SLA constraints and the assumption of shared risk link group (SRLG) failures, a novel dynamic differentiated shared path-protection algorithm (DDSP) in WDM mesh networks is proposed. Based on the basic ideas of the K-shortest path algorithm and partial SRLGdisjoint protection, DDSP can provide differentiated services for customers according to their SLA parameters while optimizing resource utilization. Simulation results show that DDSP not only can efficiently guarantee the specific SLA requirements of customers, but also can achieve significant performance gain and lead to remarkable reduction in blocking probability.