Finding persistent sparse (PS) flow is critical to early warning of various threats. Previous works have predominantly focused on either heavy or persistent flows, with limited attention given to PS flows. Although some recent studies pay attention to PS flows, they struggle to establish an objective criterion due to insufficient data-driven observations, resulting in reduced accuracy. In this paper, we define a new criterion ''anomaly boundary'' to distinguish PS flows from regular flows. Specifically, a flow whose persistence exceeds a threshold will be protected, while a protected flow with a density lower than a threshold is reported as a PS flow. We then introduce PSSketch, a high-precision layered sketch, to find PS flows. PSSketch employs variable-length bitwise counters, where the first layer tracks the frequency and persistence of all flows, and the second layer protects potential PS flows and records overflow counts from the first layer. Some optimizations have also been implemented to reduce memory consumption further and improve accuracy. The experiments show that PSSketch reduces memory consumption by 1-2 orders of magnitude compared to the strawman solution combined with existing work. Compared with SOTA solutions for finding PS flows, it outperforms up to 2.94x higher in F1 score and reduces ARE by 1-2 orders of magnitude. Meanwhile, PSSketch achieves a higher throughput than these solutions.

Blockchain has now become a critical infrastructure in Metaverse for storing and managing the digital resources of users, bridging the real and virtual worlds. However, consensus protocols in blockchains constrain the performance of their applications. While existing voting-based consensus protocols such as HotStuff and other Byzantine Fault Tolerance (BFT) protocols have optimized efficiency and scalability, they simply adopt a one-person-one-vote rule that is not aligned with the human-centric values of most blockchain applications, including Metaverse. Therefore, we propose Stake Voting Protocol (SVP), a secure and scalable consensus protocol, whose design philosophy is to consider validators’ stakes in the BFT protocol and introduce flexibility through a sliding window. We also propose a certification rule within the pipelined two-chain consensus process to enhance security. Furthermore, our epoch change and incentive mechanisms ensure dynamics and liveness, respectively. Finally, our analytical and experimental results demonstrate that the proposed SVP satisfies correctness and can resist specific attacks with low latency and high throughput.

With its wide range of applications, the Internet shows a future trend towards abundant and diverse data resources with multiple types of identifiers (multi-identifiers). However, the legacy Domain Name System (DNS) in the current TCP/IP network architecture has failed to manage these identifiers due to the centralized security issue. While some decentralized DNS alternatives have been proposed, they also face scalability issues. In this paper, we propose a blockchain-based Hierarchical Multi-Identifier System, named H-MIS, as a DNS alternative. Specially, it realizes optimal decentralization and scalability by introducing the Zero-Knowledge rollup (ZK-rollup) solution to synchronize the upper and lower on-chain identifier data, as well as off-chain associated resource data. Finally, we implement H-MIS on Ethereum and evaluate its performance. The experimental results indicate that compared to the original MIS and Ethereum Name Service (ENS), H-MIS has advantages in such aspects as efficiency, data consumption, and Gas fees.

In recent years, blockchain technology has received widespread attention. Smart contracts are programs that run on the blockchain, and their security faces serious challenges for blockchain applications. Inspired by the success of artificial intelligence technology, some smart contract vulnerability detection methods based on deep learning have been proposed and achieved meaningful progress. However, a closer look reveals three flaws in these works. First, some works simply use LSTM modules to perform sequence learning on pre-processing smart contracts, which lacks the ability to extract long-range features and does not support parallel processing of inputs. Secondly, smart contract vulnerability detection methods based on deep learning lack interpretability of results. From the perspective of system design, in order to solve these problems, we propose SmartDT, a more effective, faster, and interpretable smart contract vulnerability detection system. Specifically, (i) we propose an attention-based deep learning smart contract detection module, which is able to learn long-range dependencies in inputs and supports parallel processing of inputs. (ii) After the deep learning module, we stack an optional symbolic execution module for enhancing the interpretability of the classification results. Compared with general symbolic execution detection, our method can achieve faster detection because we can call a specific symbolic analysis model to detect input based on the classification results of the deep learning module. Extensive experiments demonstrate the effectiveness of our proposed method. Compared with other machine learningbased methods, our method achieves better performance and better interpretability, which demonstrates SmartDT’s outstanding feature learning capabilities. Compared with symbolic execution methods, our method achieves faster and more effective detection. In addition, we conducted ablation experiments to verify the effectiveness of each module.

Some Byzantine fault tolerant (BFT) consensus algorithm in consortium blockchains have lots of message broadcast to encroach bandwidth, which greatly affect the efficiency of consensus execution. Although reducing the number of global consensus nodes can help, it is also at the cost of reduced security. Based on the idea of Mimic Defense, this paper proposes a consensus framework for BFT consensus and takes the Parallel Proof of Vote (PPoV) algorithm as an example to design a randomized node sharding and role partitioning scheme to improve security of the consensus process without affecting efficiency much. In addition, the multimode adjudication group composed of the shard leaders can replace the single leader to complete voting statistics to improve the reliability of consensus results. Through theoretical analysis, it is known that the BFT consensus framework based on mimic security, taking PPoV as an example, has a strong defense effect against eclipse attacks and selfish mining. A simulation experiment is also designed to prove that through efficient iteration and dynamic threshold design, the adjudication group greatly improves the reliability of the results and the robustness of the algorithm.

In distributed storage systems, the replication mechanisms are usually used to ensure system reliability and data availability. Random replication is widely used in cloud storage systems to prevent data loss. Copyset Replication (CR) as a replication strategy, makes a nearly optimal trade-off between the number of scattered nodes and the probability of data loss. Compared with random replication, CR greatly reduces the probability of data loss caused by node failure. However, CR's random selection strategy makes it difficult to select the optimal copyset based on data characteristics such as calculation and storage. In response to this problem of CR, the Optimal Copyset Replication (OCR) proposed in this paper can select the optimal copyset according to the specified data characteristics and its corresponding node conditions. Finally, combined with Cyberspace Mimicry Defense (CMD) , we implemented OCR in a distributed object storage system and conducted related experiments. When the calculation type data reaches 300,000, the experimental results prove that compared with CR randomly selecting copyset, OCR reduces the data processing time by nearly 10% through selecting the optimal copyset. By setting relevant parameters, OCR can also ensure that the data distribution of each node is relatively uniform, and avoid data skew.

Each blockchain’s node needs to store a backup of all blocks, resulting in the whole network needs O(n) storage space, which greatly affects the nodes’ scalability. Parallel proof of vote(PPoV) is a permissioned blockchain algorithm that uses block groups as the basic data structures. This paper proposes a data lightweight scheme for PPoV. In the bock group generation stage, the BLS algorithm is used to realize signature aggregation, and the storage space of block signature and vote signature is reduced from O(n) to O(1) without affecting the performance. In the storage stage, we use erasure code to implement storage partition for block groups and ensure the real-time recoverability of complete data. To speed up reading, the timeline-based model applies different storage strategies for hot and cold data. The experimental results show that the empty block group generated by BLS aggregation signature is smaller and less sensitive to the number of nodes. And the reduction ratio of storage space under a large number of transactions can be similar to the number of normal nodes.