The growing adoption of permissioned blockchains in trade, finance, logistics has led to a proliferation of isolated networks that increasingly function as data and value silos. Achieving secure cross-chain interoperability is critical to unlocking the full potential of these ecosystems. However, existing cross-chain protocols often assume transparent state access or rely on trust assumptions that are incompatible with permissioned blockchains, which enforce strict access control and prohibit public state visibility. In this paper, we propose Pistis, a secure and verifiable framework designed to enable interoperability across unobservable permissioned blockchains. Pistis introduces a novel architecture based on decentralized oracle committees and witness network. Specifically, we propose a dynamic oracle committee election method based on verifiable random function (VRF) and a secure and verifiable cross-chain transfer protocol based on independent vote-based consensus. To support sensitive information transfer, Pistis integrates end-to-end encryption, zero-knowledge proof (ZKP), and verifiable oracle attestation. We formally analyze the security properties of Pistis and prove that it achieves strong guarantees of availability, confidentiality, and verifiability under adversarial conditions. We also implement a prototype on Hyperledger Fabric and conduct extensive experiments, showing that Pistis maintains low processing latency and high success rates across diverse adversarial and sensitive information transfer scenarios, demonstrating its feasibility in practical applications.

Cross-chain bridges have become essential infrastructure in the blockchain ecosystem, enabling interoperability across heterogeneous networks. However, externally verified bridgesthose relying on validator committeesoften expose validator identities and rely on strong trust assumptions, making them vulnerable to coercion, bribery, and targeted attacks. While prior work has improved validator reliability and decentralized trust distribution, validator anonymity remains an overlooked yet critical security concern. In this paper, we propose AnsBridge, a privacy-preserving cross-chain bridge that enables anonymous yet verifiable participation of validators. Validators register pseudonymously via a stake-based mechanism and prove ownership of secret keys through zero-knowledge proofs, without revealing their identities. Temporary public keys are added to a cryptographic accumulator, enabling anonymous eligibility validation. We further design protocols for efficient identity update and revocation using accumulator state updates and Bloom filters. We formally analyze the security of AnsBridge, demonstrating its resistance to validator-targeted attacks and its ability to preserve privacy without compromising correctness. We also implement a prototype and conduct performance evaluations, showing that AnsBridge achieves low-latency verification and compact proof sizes, making it suitable for real-world deployment. Our work highlights the importance of validator anonymity in cross-chain infrastructure and presents a practical design that balances privacy, decentralization, and efficiency.

Ethereum, a foundational blockchain platform enabling decentralized applications, has catalyzed the development of myriad applications through smart contracts. However, vulnerabilities within these contracts have precipitated notable financial losses, garnering keen interest from both industry and academia. While existing vulnerability detection research has progressed in addressing traditional challenges, it increasingly falls short of comprehensively securing smart contracts against evolving attack patterns and new vulnerabilities. Distinguishing itself from prior surveys, this review conducts an in-depth analysis of actual attack incidents, delving into the characteristics of recent vulnerabilities and thoroughly exploring the present research difficulties and challenges. The paper aims to offer a holistic view on smart contract security research, advance the depth of vulnerability detection technologies, and suggest directions for future inquiries.

Given the substantial economic repercussions resulting from smart contract security vulnerabilities, the detection and prevention of such vulnerabilities have emerged as critical issues warranting robust solutions. Recently, many researchers try to apply deep learning methods to the vulnerability detection task of smart contracts. However, deep learning-based approaches often focus on a singular feature of the smart contract source code, inhibiting a more comprehensive extraction of semantic and structural information embedded within the smart contract. The datasets employed for smart contract vulnerability detection are limited in size, which also restricts the learning capacity of the model, leading to suboptimal performance in identifying vulnerabilities related to smart contracts. To overcome these challenges, this paper introduces a novel vulnerability detection model centered on the fusion of semantic and structural features. These features, extracted from abstract syntax trees and contract graphs by Text Convolutional Neural Networks (TextCNN) and Temporal Message Propagation Network (TMP) respectively, are integrated to construct a classification prediction model. In addition, we added the pre-trained vector of SmartEmbed (smart contract similarity measurement model) to the training, and used the rich knowledge captured by the smart contract pre-trained word vector as the prior knowledge of our model, which can enhance the characterization of security features, and improve the performance of the vulnerability detection model, especially identifying hidden vulnerability rules when using limited labeled datasets. We conduct extensive experiments on approximately 5,000 smart contracts deployed on real-world Ethereum. Experiments prove that our method improves the detection accuracy and recall rate on the detection tasks of reentrancy vulnerability and timestamp dependence vulnerability, the accuracy rate reaches 78% and 89%, and the recall rate reaches 77% and 91% respectively, which is better than state-of-the-art methods.

The risk of road accidents is rising rapidly. Distracted driving remains one of the leading causes of traffic accidents. Therefore, the identifying of the distracted driving become significant. Extensive methods based on the convolutional neural network (CNN) have been applied to the detection of the distracted driving. Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visual transformer (ViT), the cascaded self-attention modules perform surpassingly in capturing content-based global interactions but unfortunately deteriorate local feature details. In order to address those challenges mentioned before, we propose a new distracted driving detection method that utilizes the driver and related object cues as guidance and combines CNN with ViT as a backbone to capture the local and global features. Besides, the simulation module is introduced to obtain the result of classification during a certain time period in the stage of inference. Under the widely used StateFarm benchmark, our proposed method presents the best performance.

With distracted driving becoming one of the main causes of traffic accidents, deep learning technology has been widely used in distracted driving detection, which achieves high accuracy when the training and test data are identically distributed. However, this assumption cannot correspond to the real-world situation. In case of a small sample size, we usually utilize open datasets as training dataset. Thus the training data distribution and test data distribution are different, which may induce accuracy plummets. Concentrating on the unforeseen data shifts encountered under different data distributions in distracted driving detection application, it is extremely desired to develop the detection technique with high robustness. In order to alleviate the issue about data shifts encountered under different data distributions, we propose an innovative method, SelectAug, to enhance images by applying the selected important features of the images. The experimental evaluations on the StateFarm dataset show that our method outperforms prior methods, demonstrating its efficacy in detecting distracted driving behaviors scenes. Furthermore, our method also improves generalization performance under different data distributions for distracted driving detection, which allows open datasets to be applied to real-world scenarios.

Due to the ever-increasing complexity of modern communication networks, network operators are making tremendous efforts on achieving objectives for the network to meet the diversified requirements of many real-world applications. However, network operators are repeatedly taking a lot of time on some common tasks shared by different networks. In order to reduce repetitive human efforts on network management, advanced machine learning paradigms, such as deep reinforcement learning, has received numerous attention in the networking community. Nevertheless, it encounters great difficulty in transferring learned policies to new environments, resulting in new model training and testing for each changed environment setting. To tackle this important issue, in this paper we propose a new framework that is the first of its kind to enable an agent to have transferable knowledge for network management, specifically, for network path selection tasks. Through this framework, an agent can efficiently learn and express the transferable network knowledge for achieving task objectives. Extensive experimental results show that the learned knowledge through the proposed framework can realize some common objectives of path selection tasks across different network environments. In addition, the knowledge learned from one network task can significantly improve the learning performance of another similar but different task.

With the development of technologies and decreasing of costs, the satellite is expected to play a fundamental role in the 5G era. On the other hand, the Information Centric Networking (ICN) is considered as a promising technology to realize 5G objectives by providing native support for efficient content retrieval while enhancing mobility supporting capability and security. In this article, we exploit the ICN architecture in a federated satellite–5G network to drive a new networking paradigm called Sat5G-ICN. Architecture of the integrated network is proposed and the feasibility of introducing ICN to the dynamic satellite forwarding plane is studied. To ensure the continuity of the ICN service, two solutions are proposed to overcome the reverse path broken problem caused by the instability of satellite links. Simulation results indicate that the proposed approaches can provide reliable ICN services through satellite links regardless of the high dynamic in the satellite constellation.

Software-defined networking (SDN) is one of the key enablers of 5G networks. With a 5G application, the interaction between the SDN controller and data plane nodes will affect the performance requirements in delay-sensitive scenarios (e.g., URLLC) and increase the network signalling overhead in other scenarios (e.g., mMTC). However, the current data plane of SDN has no capability to handle network events unless inquiring the controller. To solve this problem, we propose an enhanced SDN data plane model to enable the network with data plane response capability for network events. Specifically, the delegation of various control plane functions along with the data path can enable the delay-sensitive network event processing without the interaction with the SDN controller. This can in turn minimize the interaction between the controller and data plane nodes. Experimental results show that the proposed enhanced SDN data plane can reduce the delay and signalling overhead during network events processing, especially in delay-sensitive applications of 5G networks.

To solve the problem about supporting mobility enhancing in named data network (NDN),this paper proposed a new mobility-aware prediction approach for content prefetching (MAP-CP)and an extended approach (extended MAP-CP, eMAP-CP)of mobile consumers in NDN for the city section mobility scenario.MAP-CP and eMAP-CP predicted the future ac-cess point (AP),then pre-fetched the data that could not be received under the current AP because of the handover process and cached them in the NDN node associated with the future predicted AP,and provided nearby service for the mobile con-sumers after the handover process,to reduce the average latency from the Interest packet issued to the requested Data packet received and improve the ratio of the number of Data packets received on the total number of Interest packets issued.Based on MAP-CP,eMAP-CP could improve performance further by pre-fetching the requested data during the handover process.The simulation experiment based on ndnSIM shows that MAP-CP and eMAP-CP have lower average latency and the higher ratio than original NDN,and eMAP-CP is better than MAP-CP.When the speed is 20 m/s and the traffic is 200 pps,the average latency in MAP-CP and eMAP-CP can be reduced by 4.0%and 38.7%respectively,and the ratio can be increased by 1.2%and 4.9% respectively.

Due to the exhaustion of IPv4 address resources, the transition from IPv4 to IPv6 is inevitable and fairly urgent. Numerous transition mechanisms have been proposed to solve challenging issues of IPv6 transition. An inter-connection between IPv4 and IPv6 networks or hosts requirement has been happening throughout the IPv6 transition process. And one-time translation scheme is indispensable to achieve the inter-connection. In addition, double translation can be used in the IPv4-IPv6-IPv4 scenario. As a long-term strategy, translation scheme is important and inevitable. However, because of the diverse characteristics and transition requirements of practical networks and the lack of applicability analysis, the selection and deployment of transition mechanisms are facing with grand challenges. Targeting at those challenges, this paper investigates the basic issues and key elements of IPv6 translation transition mechanisms, and presents its first applicability index system. In particular, we analyze the applicability of existing proposed translation techniques based on the presented index system, which has significant guidance in the practical deployment of IPv6 transition techniques.

现有移动切换的相关研究方案在实际部署时配置繁琐、工作量大、网络运维成本高,有些方案还存在三角路由问题,导致切换时延增大.为了解决移动切换过程中的这些问题,提高移动切换的性能,提出了一种基于OpenFlow的移动切换框架,通过理论分析证明了该切换框架的可行性;另外,通过搭建基于OpenFlow的移动切换实验平台,与传统移动IP方案进行性能对比分析.实验结果显示,基于OpenFlow的移动切换框架能够有效地控制数据流路径,避免三角路由问题,并且较传统方案其切换时延降低,说明提出的移动切换框架能够有效节省网络带宽资源和优化移动切换效率.