With the rapid development of the Ethereum platform, phishing fraud has become increasingly rampant, posing significant security risks to both users and the platform. However, existing phishing fraud detection methods are manually designed, requiring substantial human effort, and are unable to adapt to diverse detection scenarios. In this article, we propose phishing detection on Ethereum via graph neural architecture search of transaction subgraph (PETS-GNAS). The phishing detection problem on Ethereum is transformed into a graph classification task, where accounts and transactions are represented as nodes and edges, respectively. Specifically, we acquire account labels and their corresponding transaction information from credible sources and then extract transaction subgraphs centered on labeled accounts as datasets. Subsequently, we introduce a mapping mechanism to extend these transaction subgraphs into corresponding temporal transaction subgraph (TTSG), encoding transaction attributes during the TTSG construction process. Then, graph neural architecture search (GNAS) strategy that incorporates early stopping and L2 regularization is proposed to enhance the feasibility and accuracy of Ethereum phishing detection by avoiding redundant parameters and complex architectures. Extensive experimental results demonstrate that PETS-GNAS achieves strong performance in phishing detection tasks, enabling early and accurate identification of phishing accounts.

With the rapid development of blockchain applications, the data that need to be stored increases dramatically, and the blockchain is about to face the problem of storage limitations. To deal with this problem, this paper proposes a storage scaling mechanism for Hyperledger Fabric, which relieves the storage pressure by dividing the peer nodes into clusters, and each node only stores partial data instead of the whole ledger. First, all accounting nodes are divided into clusters that include several nodes respectively, and part of the whole block data is stored in a single cluster; then, the block data are stored overlappingly on some nodes in the cluster, and each block is guaranteed to have some copies in a cluster. By arranging the copies on selected nodes according to our proposed mechanism, all the blocks are overlapped in a cluster. Theoretical analysis and simulation show that the storage volume occupied by nodes is decreased significantly in blockchain applications with frequent transactions, and in the case that the number of node failures in a single cluster does not exceed the threshold, the mechanism can still guarantee data integrity. Moreover, for applications with frequent transactions, storage space consumption can be significantly reduced without increasing excessive query time overhead.

In spatiotemporal crowdsourcing applications, sensing data uploaded by participants usually contain spatiotemporal sensitive data. If application servers publish the unprocessed sensing data directly, it is easy to expose the privacy of participants. In addition, application servers usually adopt the static publishing mechanism, which is easy to produce problems such as poor timeliness and large information loss for spatiotemporal crowdsourcing applications. Therefore, this paper proposes a spatiotemporal privacy protection (STPP) method based on dynamic clustering methods to solve the privacy protection problem for crowd participants in spatiotemporal crowdsourcing systems. Firstly, the working principles of a dynamic privacy protection mechanism are introduced. Then, based on k-anonymity and l-diversity, the spatiotemporal sensitive data are anonymized. In addition, this paper designs the dynamic k-anonymity algorithm based on the previous anonymous results. Through extensive performance evaluation on real-world data, compared with existing methods, the proposed STPP algorithm could effectively solve the problem of poor timeliness and improve the privacy protection level while reducing the information loss of sensing data.

UNLABELLED: Demultiplexing is used after high-throughput sequencing to in silico assign reads to the samples of origin based on the sequenced reads of the indices. Existing demultiplexing tools based on the similarity between the read index and the reference index sequences may fail to provide satisfactory results on low-quality datasets. We developed Bayexer, a Bayesian demultiplexing algorithm for Illumina sequencers. Bayexer uses the information extracted directly from the contaminant sequences of the targeting reads as the training dataset for a naïve Bayes classifier to assign reads. According to our evaluation, Bayexer provides higher capability, accuracy and speed on various real datasets than other tools. AVAILABILITY AND IMPLEMENTATION: Bayexer is implemented in Perl and freely available at https://github.com/HaisiYi/Bayexer.

MOTIVATION: In order to accelerate the finishing phase of genome assembly, especially for the whole genome shotgun approach of prokaryotic species, we have developed a software package designated prokaryotic genome assembly assistant system (PGAAS). The approach upon which PGAAS is based is to confirm the order of contigs and fill gaps between contigs through peptide links obtained by searching each contig end with BLASTX against protein databases. RESULTS: We used the contig dataset of the cyanobacterium Synechococcus sp. strain PCC7002 (PCC7002), which was sequenced with six-fold coverage and assembled using the Phrap package. The subject database is the protein database of the cyanobacterium, Synechocystis sp. strain PCC6803 (PCC6803). We found more than 100 non-redundant peptide segments which can link at least 2 contigs. We tested one pair of linked contigs by sequencing and obtained satisfactory result. PGAAS provides a graphic user interface to show the bridge peptides and pier contigs. We integrated Primer3 into our package to design PCR primers at the adjacent ends of the pier contigs. AVAILABILITY: We tested PGAAS on a Linux (Redhat 6.2) PC machine. It is developed with free software (MySQL, PHP and Apache). The whole package is distributed freely and can be downloaded as UNIX compress file: ftp://ftp.cbi.pku.edu.cn/pub/software/unix/pgaas1.0.tar.gz. The package is being continually updated.