Blockchain technology sparked renewed interest in planetary-scale Byzantine fault-tolerant (BFT) state machine replication (SMR). While recent works predominantly focused on improving the scalability and throughput of these protocols, few of them addressed latency. We present Mercury, a novel transformation to autonomously optimize the latency of quorum-based BFT consensus. Mercury employs a dual resilience threshold that enables faster transaction ordering when the system contains few faulty replicas. Mercury allows forming compact quorums that substantially accelerate consensus using a smaller resilience threshold. Nevertheless, Mercury upholds standard SMR safety and liveness guarantees with optimal resilience, thanks to its judicious use of a dual operation mode and BFT forensics techniques. Our experiments spread tens of replicas across continents and reveal that Mercury can order transactions with finality in less than 0.4s, half the time of a PBFT-like protocol (optimal in terms of number of communication steps and resilience) in the same network. Furthermore, Mercury matches the latency of running its base protocol on theoretically optimal internet links (transmitting at 67% of the speed of light).

Blockchain technology has renewed interest in planetary-scale Byzantine fault-tolerant (BFT) state machine replication (SMR). While recent works focus on scalability and throughput, few address latency.We present the idea of FlashConsensus, a transformation for quorum-based BFT consensus that uses an adaptive resilience threshold. FlashConsensus employs adaptive weighted replication to assign high voting power to specific replicas, thus yielding smaller quorums that speed up consensus. To maintain SMR safety and liveness guarantees with optimal resilience, FlashConsensus employs two modes of operation and BFT forensics. Experiments with replicas worldwide show FlashConsensus orders client requests in less than 0.4 s, which is half the time needed by a PBFT-like protocol with optimal consensus latency.

Several works have used machine learning techniques for network-based intrusion detection over the past few years. While proposed schemes have been able to provide high detection accuracies, they do not adequately handle the changes in network traffic behavior as time passes. Researchers often assume that model updates can be performed periodically as needed, although this is not easily feasible in real-world scenarios. This paper proposes a new intrusion detection model based on a reinforcement learning approach that aims to support extended periods without model updates. The proposal is divided into two strategies. First, it applies machine learning scheme as a reinforcement learning task to long-term learning -maintaining high reliability and high classification accuracies over time. Second, model updates are performed using a transfer learning technique coped with a sliding window mechanism that significantly decreases the need for computational resources and human intervention. Experiments performed using a new dataset spanning 8TB of data and four years of real network traffic indicate that current approaches in the literature cannot handle the evolving behavior of network traffic. Nevertheless, the proposed technique without periodic model updates achieves similar accuracy rates to traditional detection schemes implemented with semestral updates. In the case of performing periodic updates on our proposed model, it decreases the false positives up to 8%, false negatives up to 34%, with an accuracy variation up to only 6%, while demanding only seven days of training data and almost five times fewer computational resources when compared to traditional approaches.

The widespread prevalence of data breaches amplifies the importance of auditing storage systems. In this work, we initiate the study of auditable storage emulations, which provide the capability for an auditor to report the previously executed reads in a register. We precisely define the notion of auditable register and its properties, and establish tight bounds and impossibility results for auditable storage emulations in the presence of faulty storage objects. Our formulation considers loggable read-write registers that securely store data using information dispersal and support fast reads. In such a scenario, given a maximum number~$f$ of faulty storage objects and a minimum number~$τ$ of data blocks required to recover a stored value, we prove that (1) auditability is impossible if $τ\leq 2f $; (2) implementing a weak form of auditability requires $τ\geq 3f+1$; and (3) a stronger form of auditability is impossible. We also show that signing read requests overcomes the lower bound of weak auditability, while totally ordering operations or using non-fast reads enables strong auditability.

The vast datasets produced in human genomics must be efficiently stored, transferred, and processed while prioritizing storage space and restore performance. Balancing these two properties becomes challenging when resorting to traditional data compression techniques. In fact, specialized algorithms for compressing sequencing data favor the former, while large genome repositories widely resort to generic compressors (e.g., GZIP) to benefit from the latter. Notably, human beings have approximately 99.9 percent of DNA sequence similarity, vouching for an excellent opportunity for deduplication and its assets: leveraging inter-file similarity and achieving higher read performance. However, identity-based deduplication fails to provide a satisfactory reduction in the storage requirements of genomes. In this article, we balance space savings and restore performance by proposing \sf GenoDedupGenoDedup, the first method that integrates efficient similarity-based deduplication and specialized delta-encoding for genome sequencing data. Our solution currently achieves 67.8 percent of the reduction gains of SPRING (i.e., the best specialized tool in this metric) and restores data 1.62×1.62× faster than SeqDB (i.e., the fastest competitor). Additionally, GenoDedupGenoDedup restores data 9.96×9.96× faster than SPRING and compresses files 2.05×2.05× more than SeqDB.

We presentCharon, a cloud-backed storage system capable of storing and sharing big data in a secure, reliable, and efficient way using multiple cloud providers and storage repositories to comply with the legal requirements of sensitive personal data.Charonimplements three distinguishing features: (1) it does not require trust on any single entity, (2) it does not require any client-managed server, and (3) it efficiently deals with large files over a set of geo-dispersed storage services. Besides that, we developed a novel Byzantine-resilient data-centric leasing protocol to avoid write-write conflicts between clients accessing shared repositories. We evaluateCharonusing micro and application-based benchmarks simulating representative workflows from bioinformatics, a prominent big data domain. The results show that our unique design is not only feasible but also presents an end-to-end performance of up to$2.5\times$2.5×better than other cloud-backed solutions.

Despite the promising results of machine learning for network-based intrusion detection, current techniques are not widely deployed in real-world environments. In general, proposed detection models quickly become obsolete, thus, generating unreliable classifications over time. In this paper, we propose a new reliable model for semi-supervised intrusion detection that uses a verification technique to provide reliable classifications over time, even in the absence of model updates. Additionally, we cope with this verification technique with semi-supervised learning to autonomously update the underlying machine learning models without human assistance. Our experiments consider a full year of real network traffic and demonstrate that our solution maintains the accuracy rate over time without model updates while rejecting only 10.6% of instances on average. Moreover, when autonomous (non-human-assisted) model updates are performed, the average rejection rate drops to just 3.2% without affecting the accuracy of our solution.