The Proof of Work (PoW) consensus protocol stands as one of the most prevalent mechanisms in blockchain technology. However, its inherent proof mechanism inevitably leads to forking, making it vulnerable to security risks such as double-spending attacks, selfish mining, and whale attacks. Through research, blockchain characteristic parameters such as block generation time, block size, block propagation speed, number of nodes, and network connectivity will affect the fork rate of PoW blockchain. However, the existing studies only considered the effect of some of the parameters on the fork rate. There is no specific and detailed analysis of the effect factors of the fork rate, and there is no relevant quantitative evaluation. In this paper, we employ a quantitative evaluation model to delve into the effect of various characteristic parameters of the blockchain on the security of PoW in three distinct scenarios: 1) uniform computing power among all nodes; 2) selective non-participation of nodes in the computing power competition; and 3) collusion among honest nodes forming mining pools. Our analysis uncovers that smaller blocks, extended block generation time, fewer nodes, and higher network connectivity contribute to bolstering the security of the PoW blockchain. Moreover, a higher number of non-participating nodes correlates with a lower fork rate. Interestingly, collusion among nodes in mining pools exhibits no discernible effect on the fork rate of blockchain. Our findings are further validated through simulation results. The experimental results show that the block generation time should be maintained between 5 and 10 minutes and the block size should be maintained around 2 MB. For block propagation speed, the faster the better. With the increase in the number of nodes in the system, the network connectivity should be improved promptly. Furthermore, we provide security recommendations tailored for PoW blockchain designers, aimed at fortifying the resilience of their systems against potential threats.

With the wide application of the Raft consensus algorithm in blockchain systems, its safety has attracted more and more attention. However, although some researchers have formally verified the safety of the Raft consensus algorithm in most scenarios, there are still some safety problems with Raft consensus algorithm in some special scenarios, and cause problems now and then. For example, as a core part of the Raft consensus algorithm, the Raft leader election algorithm usually faces some safety problems in following scenarios: if the network communication between some nodes is abnormal, the leader node could be unstable or even cannot be elected, or the log entry cannot be updated, etc. In this paper, we model check the safety of the Raft leader election algorithm throughly using Spin. We use Promela language to model the Raft leader election algorithm and use Linear-time Temporal Logic (LTL) formulae to characterize three safety properties including stability, liveness, and uniqueness. The verification results show that the Raft leader election algorithm does not hold stability and liveness when some nodes are faulty and node log entries are inconsistent. For these safety problems, we give the suggestions for improving safety by analyzing counter examples.

Proof of Work (PoW) is the most widely used consensus protocol. However, due to the hash rate competition mechanism, longest chain principle, and transaction fee mechanism of the PoW consensus protocol, malicious nodes can launch attacks to obtain more relative revenue than honest mining, which will discourage honest miners from packing transactions into blocks and verifying blocks. As a result, the speed of the nodes reaching consensus in the network is slowed down, or even consensus cannot be reached, which ultimately affects the security of the PoW consensus protocol.In this paper, the Markov Decision Process (MDP) is used to simulate the whale attack launched by malicious nodes, and evaluate the capability of PoW consensus protocol against the whale attack. The experimental results show that the PoW consensus protocol is secure in the Bitcoin network when the transaction fee is set in the range of 0.002-0.3 block rewards and the transaction volume should not exceed 21.09 block rewards. In addition, the PoW consensus protocol will be more secure with the adjustment of parameters such as the number of block confirmations, block generation interval and block size.

Service (API) discovery and recommendation is key to the wide spread of service oriented architecture and service oriented software engineering. Service recommendation typically relies on service linkage prediction calculated by the semantic distances (or similarities) among services based on their collection of inherent attributes. Given a specific context (mashup goal), however, different attributes may contribute differently to a service linkage. In this paper, instead of training a model for all attributes as a whole, a novel approach is presented to simultaneously train separate models for individual attributes. Meanwhile, a latent attribute modeling method is developed to reveal context-aware attribute distribution. Experiments over real-world datasets have demonstrated that this fine-grained method yields higher link prediction accuracy.

Workflow is an important way to mashup reusable software services to create value-added data analytics services. Workflow provenance is core to understand how services and workflows behaved in the past, which knowledge can be used to provide a better recommendation. Existing workflow provenance management systems handle various types of provenance separately. A typical data science exploration scenario, however, calls for an integrated view of provenance and seamless transition among different types of provenance. In this paper, a graph-based, uniform provenance model is proposed to link together design-time and run-time provenance, by combining retrospective provenance, prospective provenance, and evolution provenance. Such a unified provenance model will not only facilitate workflow mining and exploration, but also facilitate workflow interoperability. The model is formalized into colored Petri nets for verification and monitoring management. A SQL-like query language is developed, which supports basic queries, recursive queries, and cross-provenance queries. To verify the effectiveness of our model, A web-based, collaborative workflow prototyping system is developed as a proof-of-concept. Experiments have been conducted to evaluate the effectiveness of the proposed SQL-like graph query against SQL query.

As more and more reusable web services are published on the Internet, how to help users quickly identify appropriate candidate services has become an increasingly critical challenge. Most of the current research efforts on service discovery rely on syntax and semantics-based service matchmaking. In contrast, this paper presents a novel way of applying network routing mechanism to facilitate service discovery, featuring scalability and performance. Services annotated by Web Ontology Language for Services (OWL-S) are organized into a network based on semantic clustering. Virtual routers are created representing clusters, and Bloom Filters are generated for service routing. A service search request is thus transformed into a network routing problem to quickly locate semantic service cluster and in turn to candidate services. In addition, the deterministic annealing technique is applied to facilitate service classification in the network construction. Dynamic network adjustment is operated to ensure the search performance in the network. Empirical study over common testbed annotated in OWL-S is reported.