As one of the most critical aspects of mobile ad hoc networks, vehicular ad hoc networks (VANETs) have attracted increasing attention with the growing demand for safety in transportation systems. Trust assessment is crucial in VANETs, as it can effectively identify and mitigate the impact of malicious vehicles, thereby ensuring the reliability and security of network communication. However, the models in most existing trust schemes cannot be dynamically updated over time, failing to adapt to the high dynamics of VANETs. Moreover, they lack protection for data privacy, increasing the risk of information tampering and leakage. To solve the above problems, this paper proposes a new Dynamic Model Update-based Trust Assessment Strategy for VANETs Considering Blockchain (DMTAS-VB). Specifically, DMTAS-VB addresses two primary aspects. Firstly, with respect to the dynamic model, the trust framework proposed in this study incorporates both direct trust and recommended trust models that are dynamically updated as interactions progress. Secondly, regarding privacy protection, blockchain is introduced into the trust model of this scheme. The innovative three-chain structure (MainBC, MesBC, and RepBC) is adopted to achieve functional separation, and core identity authentication, high-frequency message exchange, and reputation management are handled independently. The system can more flexibly optimize the performance parameters of each chain while enhancing data security and privacy protection. Simulation results demonstrate that the proposed scheme outperforms comparative approaches in detection performance under various conditions (vehicle number, vehicle speed, proportion of malicious vehicles) and different attack modes (SA, ZA, BMA, BA, and CA).

Connected and autonomous vehicles produce a substantial amount of data that is essential for implementing advanced and intelligent features. Given the importance and the volume of in-vehicle data, storing it in the cloud for later extraction as critical evidence for vehicle digital forensics is a logical choice. However, ensuring the security of forensic data against tampering and forgery attacks throughout the process is a significant challenge. Existing solutions typically assume that vehicles will generate and upload the in-vehicle data to the cloud honestly. In reality, it may be necessary to prove whether the vehicle has uploaded authentic driving-related data in case of disputes about data authenticity. To address this issue, we propose an in-vehicle digital forensic scheme with public auditing, enabling anyone to perform a public auditing algorithm to check whether the data has been modified. The proposal is based on a process-oriented data integrity proof method that enables a vehicle to generate public verifiable integrity proof. Furthermore, we evaluated the practicality of our scheme by assessing its computational and communication overhead. In terms of computational cost, our proposed scheme demonstrates a power consumption of 0.0385 kWh per 100 km at a speed of 60 km/h. Regarding communication delay, our method exhibits a 50.1% decrease compared to similar approaches.

In vehicular ad hoc networks (VANETs), the broadcasting of fake announcement messages by malicious vehicles can potentially misguide nearby vehicles. Ensuring the trustworthiness of announcement messages while preserving the privacy of vehicles is a significant challenge in the design of an announcement scheme for VANETs. To address this challenge, we propose a privacy-preserving announcement scheme with strong trustworthiness based on a new security tool called message linkable group signature with information binding and efficient revocation. Compared with the existing privacy-preserving announcement schemes, our proposed scheme not only achieves the traditional trustworthiness requirement of an announcement message but also provides strong trustworthiness, which makes it resistant to sybil and collusive attacks. To the best of our knowledge, our scheme realizes strong trustworthiness for the first time. Simulations were also performed to show the practicability of the scheme.

The security and privacy challenges faced by Vehicular Ad hoc Networks (VANETs) have led to the development of conditional privacy-preserving authentication (CPPA) schemes. Hardware security modules (HSMs) are seen as a promising solution for implementing these schemes while minimizing the burden on certificate storage. However, existing HSM-based CPPA schemes still have high computation overhead and do not meet the forward security requirements for system secret key (SSK) updates. To address these challenges, we propose an HSM-based lightweight CPPA scheme for VANETs that enjoy low computation costs. Most operations could be performed within the HSM before the message is ready to be signed, reducing real-time computation delay. The scheme also supports SSK updating using an identity-based batch multi-signature algorithm, which helps to provide forward security and vehicle revocation. Especially, the proposed SSK update scheme does not rely on any single trusted authority. Formal proof demonstrates that the proposed scheme satisfies the desired security notions. Our analysis shows that this scheme surpasses other similar ones in terms of efficiency when it comes to generating signatures.

WiFi-based human gesture recognition has recently enjoyed increasing popularity in the Internet of Things (IoT) scenarios. Simultaneously recognizing user identities and user gestures is of great importance for enhancing the system security and user quality of experience (QoE). State-of-the-art approaches that perform dual tasks suffer from increased latency or degraded accuracy in cross-domain scenarios. In this paper, we present WiDual, a dual-task system that achieves accurate cross-domain gesture recognition and user identification based on WiFi in a real-time manner. The basic idea of WiDual is to use the attention mechanism to adaptively explore cross-domain features worthy of attention for dual tasks. WiDual employs a CSI (Channel Statement Information) visualization method that transfers WiFi signals to images for further feature extraction and model training. In this way, WiDual mitigates the possible loss of useful information and excessive delays caused by extracting handcrafted features directly from the WiFi signal. Furthermore, WiDual utilizes a collaboration module to combine gesture features and user identity features to enhance the performance of dual-task recognition. We implement WiDual and evaluate its performance extensively on a public dataset including 6 gestures and 6 users performed across domains. Results show that WiDual outperforms state-of-the-art approaches, with 26% and 8% improvements on the accuracy of cross-domain user identification and gesture recognition respectively.

Mobile video analytics applications, such as smart driving, VR/AR, and video surveillance, have become increasingly popular due to the proliferation of mobile devices. These applications typically use compute-intensive Deep Neural Networks (DNNs) inference in real-time and require high accuracy. Recent studies have shown that edge computing can significantly improve the performance of these applications by offloading the computation, particularly neural network inference, from mobile devices to nearby edge servers. However, offloading continuous video streams to edge servers still faces the challenge of scarce and variable network bandwidth, resulting in high latency for mobile deep vision applications. Existing works often assume sufficient networks and powerful servers to offload all streaming computation to the edge, resulting in unsatisfactory performance in practical scenarios. In this paper, we propose EAVA, an adaptive Edge-Assisted framework on mobile devices designed for Video Analytics that considers a more practical edge situation with an unstable network environment and multiple DNN model choices. EAVA initially partitions video frame and combines mobile devices with powerful edge servers, allowing these frame partitions to parallel perform video analytics algorithms on local devices or edge servers. To handle the intricate network and inference model dynamics, EAVA trains a deep reinforcement learning model to optimize the Quality of Experience (QoE) for mobile deep vision applications, making adaptive configuration choices. Without relying on preconceived assumptions about the environment, EAVA makes optimal choices based on experiences. Finally, we implement and thoroughly evaluate the performance of EAVA using diverse real-world network traces, demonstrating its superior advantages over existing state-of-the-art solutions.

Recent research has brought to light the vulnerability of deep neural networks (DNNs) to adversarial examples. While several methods have been proposed for generating physical adversarial examples, they often suffer from a critical flaw -conspicuous and easily detectable patterns by humans, limiting their real-world effectiveness. To overcome this limitation, we introduce an innovative approach termed "dual adversarial camouflage" (DAC) that generates natural adversarial camouflage in the physical world. Our DAC method leverages natural styles to hide attacks effectively. The process involves a two-stage training process. In the first stage, we learn the style features from style images. Building on this, the second stage optimizes the camouflage obtained in the first stage by minimizing the target detection score, thus significantly enhancing the attack performance. Experiment results show that the adversarial camouflage generated by our method has high naturalness and can effectively deceive object detectors. In practical tests, the attack success rate of our adversarial camouflage in both the digital and physical worlds is impressive, achieving 96.9% and 80% respectively. This showcases the real-world potential and robustness of our DAC method in evading detection.

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU) aiming to reduce the processing delay as well as the resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications resulting in suboptimal offloading policies. In this paper, we present FEVEC, a Fast and Energy-efficient VEC framework with the objective of making the optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirement. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decision and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC, where an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Furthermore, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on realistic and simulated vehicle trajectories verify that our proposed algorithm has a better performance compared with the state-of-the-art VEC mechanism.

The Internet of Vehicles (IoV) was proposed as an approach to enable intelligent traffic management and enhance road safety. In order to achieve the intended objective of improving road safety, vehicles are required to constantly broadcast messages to the traffic management infrastructure as well as to other vehicles in the vicinity. Cybersecurity protection of the IoV system is critical as security attacks on IoV and safety-related messages could be life threatening. In this connection, it is essential to ensure the authenticity of IoV messages. Whereas, from the angle of privacy protection, it is undesirable to directly authenticate the identities of vehicles that send the IoV messages. To cope with these conflicting requirements, researchers proposed the notion of conditional anonymous authentication, which aims to authenticate message senders anonymously. When necessary, a trusted third party, named tracer, will be allowed to reveal the true identities of malicious vehicles who sent fake messages. However, existing security techniques, including pseudonyms and group signatures typically assume that the tracer is trusted. This assumption may not be desirable in situations when a curious tracer may reveal the identities of honest vehicles in the IoV system. To address this challenge, this article proposes a privacy-preserving authentication scheme with abuse-resistant tracing. Compared with existing conditional anonymous authentication schemes, our scheme prevents a single tracer from revealing the identity of vehicles. Besides, the tracing key is generated in a distributed manner, and hence no single authority in the system can reveal the true identity of a vehicle.

While cloud computing is relatively mature and its potential benefits well understood by individual, industry and government consumers, a number of security and privacy concerns remain. Unsurprisingly, designing cryptographic solutions to ensure the security of cloud services and the privacy of data outsourced to the cloud remains an ongoing research area. This paper provides a critique of the wide range of cryptographic schemes designed for securing sensitive data in the cloud computing environment, as well as outlining the research opportunities in the use of cryptographic techniques in cloud computing.

The emerging IoT stream processing is a key enabling technology for the time-critical IoT applications, which often require high accuracy and low latency. Existing stream processing engines are insufficient to meet these requirements, since they could not integrate and respond timely to variable network conditions in the dynamic wireless environment. Recent efforts focusing on adaptive streaming support user-specified policies to adapt to the variable network conditions. However, those manual-policies can hardly achieve optimal performance across a broad set of network conditions and quality of experience (QoE) objectives. In this paper, we present a Reinforcement Learning-based Adaptive streaming system (RL-Adapt) that is capable of generating adaption policies using RL-strategy and providing declarative APIs for efficient development. RL-Adapt trains a neural network model that can automatically select the optimal policy based on the observed network conditions. RL-Adapt does not rely on pre-defined models or assumptions on the environment. Instead, it learns to make decisions solely through observations of the resulting performance of past decisions. We implemented RL-Adapt and evaluated its performance extensively in three representative real-world IoT applications. Our results show that RL-Adapt outperforms the state-of-the-art scheme, with 20% improvements on average QoE.

Cloud storage provides tremendous storage resources for both individual and enterprise users. In a cloud storage system, the data owned by a user are no longer possessed locally. Hence, it is not competent to ensure the integrity of the outsourced data using traditional data integrity checking methods. A privacy-preserving public auditing protocol allows a third party auditor to check the integrity of the outsourced data on behalf of the users without violating the privacy of the data. However, existing privacy-preserving public auditing protocols assume that the end devices of users are powerful enough to compute all costly operations in real time when the data to be outsourced are given. In fact, the end devices may also be those with low computation capabilities. In this paper, we propose two lightweight privacy-preserving public auditing protocols. Our protocols are based on online/offline signatures, by which an end device only needs to perform lightweight computations when a file to be outsourced is available. Besides, our proposals support batch auditing and data dynamics. Experiments show that our protocols are hundreds of times more efficient than a recent proposal regarding to the computational overhead on user side.