With the advancement of intelligent transportation systems, location-based services (LBS) have been widely applied in vehicular ad hoc networks (VANETs). LBS utilizes mobile devices to gather vehicle location data, which is then processed using relevant technologies. By combining this data with additional information, LBS offers users personalized and intelligent services. However, providing LBS brings critical security issues related to the exposure of vehicle positions, as well as privacy-preserving problems during the process of collecting location information in VANETs. We propose a distributed trust-based k anonymity scheme to address the aforementioned issues. Our proposed scheme adopts a trust framework among vehicles for various types of LBS. This framework involves a multiparty evaluation and consideration of trust value fluctuations to enhance the efficiency of establishing a reliable k anonymous cloaking region. Furthermore, by leveraging the tamper-proof and decentralized nature of blockchain, we employ a lightweight consortium blockchain to maintain the security of the trustworthiness data throughout the entire model. Extensive security analysis and rigorous experiments have been conducted to demonstrate that the scheme exhibits a certain degree of resilience against attacks on various trust models. Additionally, it has the ability to construct anonymous regions with limited time delay, thereby preserving the privacy of vehicle locations. In comparison to other schemes, it exhibits lower computational complexity and enhanced security.

Information sharing in vehicular networks has great potential for the future Internet of Vehicles (IoV). Vehicles and roadside units (RSUs) can exchange perceptual information and driving experience to enable intelligent transportation applications such as autonomous driving and road condition analysis. However, ensuring secure and efficient information sharing among vehicles is challenging. While traditional blockchain can guarantee the tamper-proof nature of shared information, it cannot be directly applied in large-scale vehicle networks due to its slow consensus process. Therefore, we propose an information sharing approach based on a directed acyclic graph (DAG), in which shared information is encapsulated into sites instead of blocks. We also propose a driving decision-based tip selection algorithm (DDB-TSA) and design a reputation-based rate control strategy (RBRCS) to ensure secure and efficient information sharing. Simulation results show that our approach reduces consensus latency, improves information sharing efficiency, and provides a more secure information sharing environment compared to existing DAG-enabled blockchain systems.

Control-flow attestation (CFA) is a mechanism that securely logs software execution paths running on remote devices. It can detect whether a device is being control-flow hijacked by launching a challenge–response process. In the growing landscape of the Internet of Things, more and more peer devices need to communicate to share sensed data and conduct inter-operations without the involvement of a trusted center. Toward the scalability of CFA mechanisms and mitigating the single-point failure, it is important to design a decentralized CFA schema. This paper proposed a decentralized schema (CFRV) to verify the control flow on remote devices. Moreover, it introduces a token (asymmetric secret slices) into peer devices to make the attestation process mutual. In this case, CFRV can mitigate a particular kind of man-in-the-middle attack called response defraud. We built our prototype toolbox on Raspberry-Pi to formulate our proof of concept. In our evaluation, CFRV protects the verification process from malicious verifiers and the man-in-the-middle attack. The proposed mechanism can also limit the PKI (Public Key Infrastructure) usage to a single stage to save the peer devices’ computational cost. Compared to related decentralized schemes, the cryptographic operation’s duration is reduced by 40%.

Deep neural networks are susceptible to interference from deliberately crafted noise, which can lead to incorrect classification results. Existing approaches make less use of latent space information and conduct pixel-domain modification in the input space instead, which increases the computational cost and decreases the transferability. In this work, we propose an effective adversarial distribution searching-driven attack (ADSAttack) algorithm to generate adversarial examples against deep neural networks. ADSAttack introduces an affiliated network to search for potential distributions in image latent space for synthesizing adversarial examples. ADSAttack uses an edge-detection algorithm to locate low-level feature mapping in input space to sketch the minimum effective disturbed area. Experimental results demonstrate that ADSAttack achieves higher transferability, better imperceptible visualization, and faster generation speed compared to traditional algorithms. To generate 1000 adversarial examples, ADSAttack takes $11.08s$ and, on average, achieves a success rate of $98.01\%$.

The Internet of Vehicles (IoV) connects an isolated individual on the road to share information, which can improve traffic efficiency. However, the promotion of information sharing brings the critical security issues of identity authentication, followed by privacy protection issues in the authentication process in the IoV. In this study, we designed a blockchain-based conditional privacy-preserving authentication scheme for the IoV (BPA). Our scheme implements zero-knowledge proof (ZKP) to verify the identities of vehicles, which moves the authentication process down to the Roadside Units (RSUs) and achieves decentralized authentication at the edge nodes. Moreover, blockchain technology is utilized to synchronize a consistent ledger across all RSUs for recording and disseminating vehicle authentication states, which enhances the overall authentication process efficiency. We provide a theoretical analysis asserting that the BPA ensures enhanced security and effectively protects the privacy of all participating vehicles. Experimental evaluations confirm that our scheme outperforms existing solutions in terms of the computational and communication overhead.

With the evolution of cloud-native microservice architectures traditional sidecar-based monitoring patterns and fragmented security tools have introduced significant resource overhead and management complexity. Current research indicates that the sidecar pattern induces notable network latency in distributed tracing while hybrid deployments of multiple kernel-level security tools often lead to policy execution conflicts and performance bottlenecks. Furthermore native probing mechanisms in current container orchestration platforms lack precise awareness of internal application states and malware detection methods based on simple system call counting struggle to identify highly concealed advanced persistent threats. To address these limitations this paper proposes an adaptive and unified framework for full-stack observability and security driven by extended Berkeley Packet Filter technology. This framework innovatively constructs a kernel-level closed loop for monitoring analysis and response. First a multi-modal data fusion adaptive sensing mechanism is proposed to unify the collection of application-layer network flows and system call sequences within the kernel space. This approach eliminates the need for sidecars while providing context-rich feature data for anomaly detection models. Second a state-aware dynamic hook orchestration algorithm is designed. Operating in a low-load monitoring mode by default this algorithm automatically escalates hook depth to the Linux Security Module layer for active blocking or granular forensics upon detecting service anomalies or behavioral drift effectively solving the performance dilemma of continuous high-load monitoring. Finally a graph-theory-driven unified policy arbitration engine is constructed to pre-calculate and resolve logical conflicts between security defense policies and operational scheduling policies prior to bytecode loading. Experimental results demonstrate that the proposed framework integrates full-stack observability and real-time security defense within a single agent significantly reducing end-to-end latency for anomaly detection and response while drastically lowering CPU overhead effectively defending against complex supply chain attacks and resource exhaustion attacks while maintaining a low false-positive rate.