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In recent years, with the rapid development of blockchain technology, the issues of storage load and data security have attracted increasing attention. Due to the immutable nature of data on the blockchain, where data can only be added and not deleted, there is a significant increase in storage pressure on blockchain nodes. In order to alleviate this burden, this paper proposes a blockchain data storage strategy based on a hot and cold block mechanism. It employs a block heat evaluation algorithm to assess the historical and correlation-based heat indicators of blocks, enabling the identification of frequently accessed block data for storage within the blockchain nodes. Conversely, less frequently accessed or "cold" block data are offloaded to cloud storage systems. This approach effectively reduces the overall storage pressure on blockchain nodes. Furthermore, in applications such as healthcare and government services that utilize blockchain technology, it is essential to encrypt stored data to safeguard personal privacy and enforce access control measures. To address this need, we introduce a blockchain data encryption storage mechanism based on threshold secret sharing. Leveraging threshold secret sharing technology, the encryption key for blockchain data is fragmented into multiple segments and distributed across network nodes. These encrypted key segments are further secured through additional encryption using public keys before being stored. This method serves to significantly increase attackers' costs associated with accessing blockchain data. Additionally, our proposed encryption scheme ensures that each block has an associated encryption key that is stored alongside its corresponding block data. This design effectively mitigates vulnerabilities such as weak password attacks. Experimental results demonstrate that our approach achieves efficient encrypted storage of data while concurrently reducing the storage pressure experienced by blockchain nodes. [ABSTRACT FROM AUTHOR]

This paper proposes a linear consensus protocol QuickBFT based on Vague sets and multi-attribute decision-making methods. QuickBFT simplifies the communication process based on the HotStuff protocol, reduces the four-stage communication to three-stage communication, and reduces the consensus delay. Furthermore, we introduce the Vague set and multi-attribute decision-making theory into the consensus protocol and propose a new leader node selection algorithm, which can prevent Byzantine nodes from becoming leader nodes, thereby improving the protocol performance when the leader node is attacked. Experimental results show that the throughput of QuickBFT is slightly higher than that of the HotStuff protocol without Byzantine nodes, and the consensus delay is reduced by 20%. In the presence of Byzantine nodes, the throughput of QuickBFT is increased by 80% compared with the HotStuff protocol, and the consensus delay is reduced by 60%. [ABSTRACT FROM AUTHOR]

Digital electronic detonators, as a civil explosive, are of prime importance for people's life and property safety in the process of production and operation. Therefore, the Ministry of Industry and Information Technology and the Ministry of Public Security of the People's Republic of China have extremely high requirements for their essential safety. Existing schemes are vulnerable to tampering and single points of failure, which makes tracing unqualified digital electronic detonators difficult and identifying the responsibility for digital electronic detonator accidents hard. This paper presents a digital electronic detonator safety supervision system based on a consortium blockchain. To achieve dynamic supply chain supervision, we propose a novel digital electronic detonator supervision model together with three codes in one. We also propose a blockchain-based system that employs smart contracts to achieve efficient traceability and ensure security. We implemented the proposed model using a consortium blockchain platform and provide the cost. The evaluation results validate that the proposed system is efficient. [ABSTRACT FROM AUTHOR]

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks. [ABSTRACT FROM AUTHOR]

Hands-on experiments are essential for computer network security education. Existing laboratory solutions usually require significant effort to build, configure, and maintain and often do not support reconfigurability, flexibility, and scalability. This paper presents a cloud-based virtual laboratory education platform called V-Lab that provides a contained experimental environment for hands-on experiments using virtualization technologies (such as Xen or KVM Cloud Platform) and OpenFlow switches. The system can be securely accessed through OpenVPN, and students can remotely control the virtual machines (VMs) and perform the experimental tasks. The V-Lab platform also offers an interactive Web GUI for resource management and a social site for knowledge sharing and contribution. By using a flexible and configurable design, V-Lab integrates pedagogical models into curriculum design and provides a progressive learning path with a series of experiments for network security education. Since summer 2011, V-Lab has served more than 1000 students from six courses across over 20 experiments. The evaluation demonstrates that the platform and curriculum have produced excellent results and helped students understand and build up computer security knowledge to solve real-world problems.

Summary Nowadays, Blockchain technology has received widespread attention because of its ability to effectively solve the trust problem in transactions. However, the throughput of crypto currency using Blockchain technology has never been comparable to that of centralized payment institutions. The fundamental reason is the architecture of the Blockchain itself, and the existing methods often focus on the part of the Blockchain but lack the overall control. In this paper, we design, a novel Recursively Reinforced Blockchain Architecture Search (BORAS) architecture, from the perspective of the overall structure of the Blockchain, which can search the various attributes of the various levels in a adaptive manner, aiming at selecting the best configuration in a dynamical environment. First, we introduce a recursive and scalable reinforcement learning framework, to find the optimal block size and block interval of the Blockchain, so as to improve the throughput of the Blockchain system. Second, we add the selection scheme of consensus algorithm to the dynamic space and also use the framework to select the optimal consensus mechanism to realize the optimal configuration of the Blockchain system in the dynamic environment. Third, we adopt a recursive method, which greatly reduces the time complexity required for the optimal configuration of the Blockchain system and improves the configuration efficiency. Comprehensive experiments on three representative Blockchain platforms demonstrate that BOBAS can consistently improve the throughput, while also reducing latency, with remarkable and efficient performances. [ABSTRACT FROM AUTHOR]

Powered by the development of the fifth-generation mobile communication technology (5G), the Internet of things (IoT) has been widely applied in people's life. Due to the limitation of storage and computing power, the data transmission of IoT devices faces challenges in terms of security and privacy. Therefore, many researchers provide the conjunction of blockchain and mobile edge computing (MEC) to make up for the lack of computing and security. MEC can meet the storage and computing requirements for IoT devices. Blockchain can provide a decentralized, antitamper solution that can help devices overcome security deficiencies, whereas the speed of blockchain communication is not fast enough because of the consensus mechanism. In this article, we focus on the permissioned blockchain and propose an optimized bandwidth allocation algorithm to promote the performance of consensus communication. The algorithm contains an In-Network control ideology and supports deployment on MEC servers. Deep reinforcement learning (DRL) is employed to perform the computation of available bandwidth in our scheme. We implement a prototype system in the testbed and perform a simulation, and the results show the advantages compared with the current widely used algorithm. By applying our method, the Internet of things devices can transmit data safely and efficiently. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes an algebraic system, verification algebra (VA), for reducing the number of component combinations to be verified in multi‐tenant architecture (MTA). MTA is a design architecture used in SaaS (Software‐as‐a‐Service) where a tenant can customize its applications by integrating services already stored in the SaaS databases or newly supplied services. Similar to SaaS, VaaS (Verification‐as‐a‐Service) is a verification service in a cloud that leverages the computing power offered by a cloud environment with automated provisioning, scalability and service composition. In VaaS architecture, however, there is a challenging problem called 'combinatorial explosion' that it is difficult to verify a large number of compositions constructed by both quantities of components and various combination structures even with computing resources in cloud. This paper proposes rules to emerge combinations status for future verification, on the basis of the existing results. Both composition patterns and properties are considered and analysed in VA rules. [ABSTRACT FROM AUTHOR]

Smart contracts (SCs) run on the distributed ledger technology (DLT) platform and can implement agreements between participants without a trusted third party. This paper uses the DLT and SC techniques to build distributed applications composed of existing services. In practice, there are many functionally-equivalent services on the Internet. To beat their competitors, the service providers usually offer flexible QoS and use dynamic pricing strategies. Moreover, the service providers can change at runtime, e.g., they may encounter problems so that their QoS drops suddenly. This makes achieving the optimization goal at runtime (e.g., the maximization of the utility) more difficult. To address this problem, first, this paper proposes an SC-based negotiation framework. The SCs can ensure that the transactions are automatically and reliably performed as agreed upon between the service requesters and providers. The DLTs can provide the reliable data of the requests and responses of the service requesters and providers to the SCs. In addition, the SCs can identify the troubled service providers, and find other service providers to replace them at runtime. Second, this paper proposes a Bayesian Nash equilibrium (BNE) of the service providers. In the BNE, the cost-efficient service providers offer the high QoS the service requester asks for and report their costs truthfully. This BNE enables the selection of the cost-efficient service providers and the achievement of the (near) maximization of the service requesters' utility. This paper implements the proposed negotiation framework on a DLT platform called Hyperledger Fabric. The experiment results demonstrate that the proposed approach outperforms the existing approaches and can adapt to the changes of the service providers. [ABSTRACT FROM AUTHOR]

A new algebraic system, test algebra ($\mathsf{TA}$), is proposed for identifying faults in combinatorial testing for software-as-a-service (SaaS) applications. In the context of cloud computing, SaaS is a new software delivery model, in which mission-critical applications are composed, deployed, and executed on cloud platforms. Testing SaaS applications is challenging because new applications need to be tested once they are composed, and prior to their deployment. A composition of components providing services yields a configuration providing an SaaS application. While individual components in the configuration may have been thoroughly tested, faults still arise due to interactions among the components composed, making the configuration faulty. When there are $k$ components, combinatorial testing algorithms can be used to identify faulty interactions with $t$ or fewer components, for some threshold $2 \leq t \leq k$ on the size of interactions considered. In general, these methods do not identify specific faults, but rather indicate the presence or absence of some faults. To identify specific faults, an adaptive testing regime repeatedly constructs and tests configurations in order to determine, for each interaction of interest, whether it is faulty or not. In order to perform such testing in a loosely coupled distributed environment such as the cloud, it is imperative that testing results can be combined from many different servers. The $\mathsf{TA}$ defines rules to permit results to be combined, and to identify the faulty interactions. Using the $\mathsf{TA}$ , configurations can be tested concurrently on different servers and in any order. The $\mathsf{TA}$ always keeps the high reduction rate of potential faulty configurations in fault location analysis. [ABSTRACT FROM AUTHOR]