Consistency Is Not Enough in Byzantine Fault Tolerance

Modern payments systems are critical infrastructure for the US and global economy, and they all utilize computing systems to facilitate transactions. These computing systems can be vulnerable to failures and an outage of a payment system could cause a serious ripple effect throughout the economy it supports. Commonly used designs in existing distributed computer systems often lack a built-in defense against certain types of failures (e.g., malicious attacks and silent data corruption) and rely on preventing these failures from happening in the first place via techniques external to the system itself. These computer system failures can cause downtime in the systems (e.g., modern payments systems) that rely on them. Byzantine Fault Tolerant (BFT) systems have the potential of improved resiliency and security. BFT systems can tolerate a larger range of failure modes than contemporary designs but suffer from performance challenges. Our work sought to design and evaluate a scalable BFT architecture and compare its properties to other database architectures used in payments infrastructure. This analysis is intended to better understand technical tradeoffs and is agnostic to broader policy or operational considerations. In this paper, we present Heraclius, a parallelizable leader-based, BFT key-value store that could be extended for use in payment systems. Heraclius executes transactions in parallel to achieve high transaction volumes. We analyze the scalability of the protocol, bottlenecks and potential solutions to the bottlenecks. We ran the prototype implementation with up to 256 nodes and achieved a transactional volume of 110 thousand operations per second with a transaction latency 0.2 seconds

The primary concern of traditional Byzantine fault tolerance is to ensure strong replica consistency by executing incoming requests sequentially according to a total order. Speculative execution at both clients and server replicas has been proposed as a way of reducing the end-to-end latency. In this article, we introduce optimistic Byzantine fault tolerance. Optimistic Byzantine fault tolerance aims to achieve higher throughput and lower end-to-end latency by using a weaker replica consistency model. Instead of ensuring strong safety as in traditional Byzantine fault tolerance, nonfaulty replicas are brought to a consistent state periodically and on-demand in optimistic Byzantine fault tolerance. Not all applications are suitable for optimistic Byzantine fault tolerance. We identify three types of applications, namely, realtime collaborative editing, event stream processing, and services constructed with conflict-free replicated data types, as good candidates for applying optimistic Byzantine fault tolerance. Furthermore, we provide a design guideline on how to achieve eventual consistency and how to recover from conflicts at different replicas. In optimistic Byzantine fault tolerance, a replica executes a request immediately without first establishing a total order of the message, and Byzantine agreement is used only to establish a common state synchronization point and the set of individual states needed to resolve conflicts. The recovery mechanism ensures both replica consistency and the validity of the system by identifying and removing the operations introduced by faulty clients and server replicas.

Blockchain has been widely used in finance, the Internet of Things (IoT), supply chains, and other scenarios as a revolutionary technology. Consensus protocol plays a vital role in blockchain, which helps all participants to maintain the storage state consistently. However, with the improvement of network environment complexity and system scale, blockchain development is limited by the performance, security, and scalability of the consensus protocol. To address this problem, this paper introduces the collaborative filtering mechanism commonly used in the recommendation system into the Practical Byzantine Fault Tolerance (PBFT) and proposes a Byzantine fault-tolerant (BFT) consensus protocol based on collaborative filtering recommendation (CRBFT). Specifically, an improved collaborative filtering recommendation method is designed to use the similarity between a node’s recommendation opinions and those of the recommender as a basis for determining whether to adopt the recommendation opinions. This can amplify the recommendation voice of good nodes, weaken the impact of cunning malicious nodes on the trust value calculation, and make the calculated results more accurate. In addition, the nodes are given voting power according to their trust value, and a weight random election algorithm is designed and implemented to reduce the risk of attack. The experimental results show that CRBFT can effectively eliminate various malicious nodes and improve the performance of blockchain systems in complex network environments, and the feasibility of CRBFT is also proven by theoretical analysis.

This chapter introduces two different algorithms to detect intrusions in mission critical communication systems to guarantee their security. The first algorithm is a classification algorithm which applies the concept of supervised learning. The second algorithm is a clustering algorithm which applies the concept of unsupervised learning. The algorithms detect intrusions using a set of detection rules that are structured in the form of decision trees. The algorithms are described in details and their results on well-known dataset are introduced. An enhancement for the J48algorithm is also introduced, where the decision tree for the algorithm is changed to a binary tree. The change enhances the complexity to reach a decision. The chapter includes a brief introduction about the security in Mission critical systems and the reason behind securing such systems. It introduces different methodologies that were introduced to detect intrusions in wireless communications.

Existing Byzantine fault tolerant (BFT) storage solutions that achieve strong consistency and high availability, are costly compared to solutions that tolerate simple crashes. This cost is one of the main obstacles in deploying BFT storage in practice.In this paper, we present PoWerStore, a robust and efficient data storage protocol. PoWerStore's robustness comprises tolerating network outages, maximum number of Byzantine storage servers, any number of Byzantine readers and crash-faulty writers, and guaranteeing high availability (wait-freedom) and strong consistency (linearizability) of read/write operations. PoWerStore's efficiency stems from combining lightweight cryptography, erasure coding and metadata write-backs, where readers write-back only metadata to achieve strong consistency. Central to PoWerStore is the concept of ``Proofs of Writing'' (PoW), a novel data storage technique inspired by commitment schemes. PoW rely on a 2-round write procedure, in which the first round writes the actual data and the second round only serves to ``prove'' the occurrence of the first round. PoW enable efficient implementations of strongly consistent BFT storage through metadata write-backs and low latency reads.We implemented PoWerStore and show its improved performance when compared to existing robust storage protocols, including protocols that tolerate only crash faults.

A long-standing promise of Byzantine Fault-Tolerant (BFT) replication is to maintain the service correctness despite the presence of malicious failures. The key challenge here is how to ensure replicas fail independently, i.e., avoid that a single attack compromises more than f replicas at once. The obvious answer for this is the use of diverse replicas, but most works in BFT simply assume such diversity without supporting mechanisms to substantiate this assumption. Lazarus is a control plane for managing the deployment and execution of diverse replicas in BFT systems. Lazarus continuously monitors the current vulnerabilities of the system replicas (reported in security feeds such as NVD and ExploitDB) and employs a metric to measure the risk of having a common weakness in the replicas set. If such risk is high, the set of replicas is reconfigured. Our evaluation shows that the devised strategy reduces the number of executions where the system becomes compromised and that our prototype supports the execution of full-fledged BFT systems in diverse configurations with 17 OS versions, reaching a performance close to a homogeneous bare-metal setup.

Byzantine fault-tolerant storage systems can provide high availability in hazardous environments, but the redundant servers they require increase software development and hardware costs. In order to minimize the number of servers required to implement fault-tolerant storage services, we develop a new algorithm that uses a “Listeners” pattern of network communication to detect and resolve ordering ambiguities created by concurrent accesses to the system. Our protocol requires 3f + 1 servers to tolerate up to f Byzantine faults—f fewer than the 4f + 1 required by existing protocols for non-self-verifying data. In addition, SBQ-L provides atomic consistency semantics, which is stronger than the regular or pseudo-atomic semantics provided by these existing protocols. We show that this protocol is optimal in the number of servers— any protocol that provides safe semantics or stronger requires at least 3f + 1 servers to tolerate f Byzantine faults in an asynchronous system. Finally, we examine a non-confirmable writes variation of the SBQ-L protocol where a client cannot determine when its writes complete. We show that SBQ-L with non-confirmable writes provides regular semantics with 2f + 1 servers and that this number of servers is minimal.

Many state-machine Byzantine Fault Tolerant (BFT) protocols have been introduced so far. Each protocol addressed a different subset of conditions and use-cases. However, if the underlying conditions of a service span different subsets, choosing a single protocol will likely not be a best fit. This yields robustness and performance issues which may be even worse in services that exhibit fluctuating conditions and workloads. In this paper, we reconcile existing state-machine BFT protocols in a single adaptive BFT system, called ADAPT, aiming at covering a larger set of conditions and use-cases, probably the union of individual subsets of these protocols. At anytime, a launched protocol in ADAPT can be aborted and replaced by another protocol according to a potential change (an event) in the underlying system conditions. The launched protocol is chosen according to an 'evaluation process' that takes into consideration both: protocol characteristics and its performance. This is achieved by applying some mathematical formulas that match the profiles of protocols to given user (e.g., service owner) preferences. ADAPT can assess the profiles of protocols (e.g., throughput) at run-time using Machine Learning prediction mechanisms to get accurate evaluations. We compare ADAPT with well known BFT protocols showing that it outperforms others as system conditions change and under dynamic workloads. © 2015 IEEE.

Because of its excellent properties of fault tolerance, efficiency and availability, the practical Byzantine fault tolerance (PBFT) algorithm has become the mainstream consensus algorithm in blockchain. However, current PBFT algorithms have problems such as inadequate security of primary node selection, high communication overhead and network delay in the process of consensus. To address these problems, we design a novel efficient Byzantine fault tolerance algorithm based on credit grouping, called CG-PBFT. First, we propose a new credit evaluation model to obtain nodes’ credit values and introduce an optimized three-way quick sorting algorithm to divide nodes into the master-node group, the consensus-node group and the observation-node group, which have different privileges. The nodes in the observation-node group are restricted from participating in consensus, which reduces the communication overhead and improves consensus efficiency. Second, we propose an optimized selection method for the primary node based on a voting mechanism whereby the consensus-node group and observation-node group vote to produce the primary node, which reduces the probability of malicious nodes acting as the primary node and improves the security of primary node selection. Finally, the identity conversion mechanism between node groups is designed, and the actual behavior of nodes within different groups is given credit rewards or punishment, so as to keep an incentive for nodes to participate in appropriate system behavior and improve the working enthusiasm of nodes. The experimental simulation results show that compared with existing PBFT algorithms, the CG-PBFT algorithm improves the average throughput by 51.3% and reduces the average delay by 64.5%; it greatly improves the operating efficiency of the system and can be more suitable for application in the consortium blockchain scenarios.

Although the consortium blockchains commonly adopt consensus algorithms with Byzantine fault tolerance (such as practical Byzantine fault tolerance (PBFT)), its consensus efficiency will be degraded by the existence of malicious nodes or behaviors. However, the existing researches mainly focus on the detection of malicious behaviors for public blockchains, but are rare about consortium blockchains. In this paper, an anomaly detection model based on machine learning (ML) classification algorithm is proposed for consortium blockchains that adopt PBFT. Besides, a two-stage process is proposed to reduce the resource consumption for anomaly detection. The data needed for proposed model only has two dimensions and is convenient to obtain. The results of experiment show that ML is very effective in anomaly detection for consortium blockchains. Specifically, the algorithms with the highest accuracy are convolutional neural networks (CNN), k-nearest neighbor (KNN) and support vector machines (SVM) in turn. However, KNN and SVM are more suitable because resource consumption of both algorithms are one third of CNN, and the accuracy rates are above 0.9 which is 0.9% lower than CNN.

DRBFT: Delegated randomization Byzantine fault tolerance consensus protocol for blockchains

The decentralized and immutable nature of blockchain technology offers promising avenues for secure healthcare data sharing among legitimate institutions. However, current blockchain systems face critical bottlenecks in handling large volumes of medical data, low storage efficiency, and vulnerabilities in security. To overcome these challenges, a novel healthcare blockchain data-sharing scheme integrating Secure Multi-party Computation (SMPC) is proposed. To mitigate the risk of data leaks resulting from key compromise, Blakley's secret sharing technique is employed for key distribution management, and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is integrated for access control. To address the challenge of handling large data on the chain, data that needs to be uploaded to the blockchain is encoded and fragmented using erasure coding techniques. Additionally, a Byzantine Fault Tolerance mechanism based on Reputation and Polling verification (TRP-PBFT) is introduced to enhance data sharing efficiency and system robustness. Simulation experiments conducted on platforms like Fabric demonstrate that this solution improves data sharing efficiency while ensuring data confidentiality and integrity. In comparison to Practical Byzantine Fault Tolerance (PBFT) under similar conditions, TRP-PBFT reduces the average latency from 655.42 ms to 342.22 ms and increases the average throughput from 107.31 TPS to 143.45 TPS. The scheme maintains high throughput and offers relatively stable latency performance.

Practical Byzantine Fault Tolerance (PBFT) has been widely used in consortium blockchain systems; however, it suffers from performance degradation and susceptibility to Byzantine faults in complex environments. To overcome these limitations, this paper proposes RE-BPFT, an enhanced consensus algorithm that integrates a nuanced node credibility model considering direct interactions, indirect reputations, and historical behavior. Additionally, we adopt an optimized ID3 decision-tree method for node classification, dynamically identifying high-performing, trustworthy, ordinary, and malicious nodes based on real-time data. To address issues related to centralization risk in leader selection, we introduce a weighted random primary node election mechanism. We implemented a prototype of the RE-BPFT algorithm in Python and conducted extensive evaluations across diverse network scales and transaction scenarios. Experimental results indicate that RE-BPFT markedly reduces consensus latency and communication costs while achieving higher throughput and better scalability than classical PBFT, RBFT, and PPoR algorithms. Thus, RE-BPFT demonstrates significant advantages for large-scale and high-demand consortium blockchain use cases, particularly in areas like digital traceability and forensic data management. The insights gained from this study offer valuable improvements for ensuring node reliability, consensus performance, and overall system resilience.

The 2nd Workshop on Recent Advances on Intrusion-Tolerant Systems aims to bring together researchers in the related areas of Intrusion Tolerance, Distributed Trust, Survivability, Byzantine Fault Tolerance, and Resilience. These areas have the purpose of enhancing the Dependability and Security of computer systems by tolerating both malicious faults (attacks, intrusions) and accidental faults. The workshop will be specially interested in intrusion-tolerant systems: how to build them? How to evaluate and test their dependability and security? What systems need to be intrusion-tolerant? It will provide a forum for researchers in these areas to present recent results, discuss open problems that still need research, the steps that need to be taken for intrusion-tolerant systems to be deployed in practice, and the target application domains for intrusion tolerance. The program consists of 3 position papers, one keynote speech by Manuel Costa from Microsoft Research, and one invited talk by Jonathan M. McCune from the Carnegie Mellon University, who we warmly thank for being present at the workshop.

Monte Carlo (MC) method is widely used in resolving mathematical problems that are too complicated to solve analytically. The method involves with sampling process of the random numbers and probability to estimate the result. As MC method depending on a large number of good quality random numbers to produce a high accuracy result, developing a good random number generator (RNG) is vital. Most of random number generators are developed in software based, with the improvement of Field Programmable Gate Arrays (FPGA) density and speed in recent days, implementing random number generator (RNG) directly into hardware is feasible. Random Walk on the Boundary (WOB) is one of MC methods that applied to calculate the unit cube capacitance. The unique requirement of this method is the random numbers produced by RNG must fall within the Gaussian distribution and the maximum decimal values in the range of [0, 1]. Thus, in this paper we presented a novel hardware RNG for Random WOB method to calculate unit cube capacitance in FPGA. The RNG is implemented in floating-point base, using the combination of Cellular Automata Shift Register (CASR) and Linear Feedback Shift Register (LFSR) then channeled through Box-Muller transformation. There is linear approximation in computing logarithmic function applied in Box-Muller transformation. Based on statistical tests, the random numbers generated is nearly 97.5% resembling the standard normal distribution.