Blockchain Network Propagation Mechanism Based on P4P Architecture

Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain’s performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay.

CFM-RFM: A Cascading Failure Model for Inter-Domain Routing Systems with the Recovery Feedback Mechanism

With the increase and diversification of network users, the scale of the inter-domain routing system is becoming larger and larger. Cascading failure analysis and modeling are of great significance to improve the security of inter-domain routing networks. To solve the problem that the propagation principle of cascading failure does not conform to reality, a Cascading Failure Model for inter-domain routing systems with the Recovery Feedback Mechanism (CFM-RFM) is proposed in this paper. CFM-RFM comprehensively considers the main factors that cause cascade failure. Based on two types of update message propagation mechanism and traffic redistribution, it simulates the cascading failure process. We found that under the action of the recovery feedback mechanism, the cascading failure process was accelerated, and the network did not quickly return to normal, but was rather quickly and extensively paralyzed. The average attack cost can be reduced by 38.1% when the network suffers the same damage.

The effect of spokesperson attribution on public health message sharing during the COVID-19 pandemic

It is urgent to understand how to effectively communicate public health messages during the COVID-19 pandemic. Previous work has focused on how to formulate messages in terms of style and content, rather than on who should send them. In particular, little is known about the impact of spokesperson selection on message propagation during times of crisis. We report on the effectiveness of different public figures at promoting social distancing among 12,194 respondents from six countries that were severely affected by the COVID-19 pandemic at the time of data collection. Across countries and demographic strata, immunology expert Dr. Anthony Fauci achieved the highest level of respondents’ willingness to reshare a call to social distancing, followed by a government spokesperson. Celebrity spokespersons were least effective. The likelihood of message resharing increased with age and when respondents expressed positive sentiments towards the spokesperson. These results contribute to the development of evidence-based knowledge regarding the effectiveness of prominent official and non-official public figures in communicating public health messaging in times of crisis. Our findings serve as a reminder that scientific experts and governments should not underestimate their power to inform and persuade in times of crisis and underscore the crucial importance of selecting the most effective messenger in propagating messages of lifesaving information during a pandemic.

Message propagation in DTN based on virtual contact of behavior model

Delay Tolerant Network (DTN) is a kind of network structured to deliver message intermittently. Network connections are not persistent between nodes, instead they must rely on nodes making geographic location movements to incur contact with other nodes and establish intermittent communication sessions to allow messages delivery. We will refer to encounters via geographic location movements as ?physical contact.? Many DTN researches mainly focus on message delivery via physical contact. However, this paper believes that in a realistic environment, encounters between nodes not only happen geographically in nature, but also occur virtually in cyberspace. When both nodes go online on the same social media platform, it is an encounter we refer as virtual contact. How messages deliver for virtual contact is store-post-and-forward, just like what happens in a DTN, but it is no longer restrained by geographical locations. This paper considers a scenario in which nodes make virtual contact in cyberspace and incur message delivery based on their own behavior patterns. The verifying experiment is conducted using both survey and simulation. First of all, we handed out questionnaires for students to fill out. The questionnaire inquired them to rank their most frequent activities performed on social media platforms. According to the responses, we conclude the top 3 frequent activities when the students use social media platforms and classify them into 3 groups according to a weighted behavior pattern scheme. The classification includes Social Group, Read-Only Group and Interest Group. It does not matter which group a student is assigned to. In the simulation, he or she will get to decide whether to deliver/receive messages or not based on a randomized selection on 3 behavior pattern. Finally, we analyze the simulation result to determine how messages propagated in different behavior pattern groups. It is derived from the simulation that to quicken message propagation, directing messages to one of the behavior groups yields the maximize benefits. This provides the basis for further researches on collecting data of desired scenarios to establish respective propagation models.

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

A small fault in a large communication network may cause abrupt and large alarms, making the localization of the root cause of failure a difficult task. Traditionally, fault localization is carried out by an operator who uses alarms in alarm lists; however, fault localization process complexity needs to be addressed using more autonomous and intelligent approaches. Here, we present an overall framework that uses a message propagation mechanism of belief networks to address fault localization problems in communication networks. The proposed framework allows for knowledge storage, inference, and message transmission, and can identify a fault’s root cause in an event-driven manner to improve the automation of the fault localization process. Avoiding the computational complexity of traditional Bayesian networks, we perform fault inference in polytrees with a noisy OR-gate model (PTNORgate), which can reduce computational complexity. We also offer a solution to store parameters in a network parameter table, similar to a routing table in communication networks, with the aim of facilitating the development of the algorithm. Case studies and a performance evaluation show that the solution is suitable for fault localization in communication networks in terms of speed and reliability.

Smart Contract Vulnerability Detection using Graph Neural Network

The security problems of smart contracts have drawn extensive attention due to the enormous financial losses caused by vulnerabilities. Existing methods on smart contract vulnerability detection heavily rely on fixed expert rules, leading to low detection accuracy. In this paper, we explore using graph neural networks (GNNs) for smart contract vulnerability detection. Particularly, we construct a contract graph to represent both syntactic and semantic structures of a smart contract function. To highlight the major nodes, we design an elimination phase to normalize the graph. Then, we propose a degree-free graph convolutional neural network (DR-GCN) and a novel temporal message propagation network (TMP) to learn from the normalized graphs for vulnerability detection. Extensive experiments show that our proposed approach significantly outperforms state-of-the-art methods in detecting three different types of vulnerabilities.