Byzantine-Resistant Total Ordering Algorithms

AbstractMulticast group communication protocols are used extensively in fault-tolerant distributed systems. For many such protocols, the acknowledgments for individual messages define a causal order on messages. Maintaining the consistency of information, replicated on several processors to protect it against faults, is greatly simplified by a total order on messages. We present algorithms that incrementally convert a causal order on messages into a total order and that tolerate both crash and Byzantine process faults. Varying compromises between latency to message ordering and resilience to faults yield four distinct algorithms. All of these algorithms use a multistage voting strategy to achieve agreement on the total order and exploit the random structure of the causal order to ensure probabilistic termination

Time as a guide to cause

How do people learn causal structure? In two studies we investigated the interplay between temporal order, intervention and covariational cues. In Study 1 temporal order overrode covariation information, leading to spurious causal inferences when the temporal cues were misleading. In Study 2 both temporal order and intervention contributed to accurate causal inference, well beyond that achievable through covariational data alone. Together the studies show that people use both temporal order and interventional cues to infer causal structure, and that these cues dominate the available statistical information. We endorse a hypothesis-driven account of learning, whereby people use cues such as temporal order to generate initial models, and then test these models against the incoming covariational data

Loupe: Verifying Publish-Subscribe Architectures with a Magnifying Lens

CONE

Exact Inference Techniques for the Analysis of Bayesian Attack Graphs

Attack graphs are a powerful tool for security risk assessment by analysing network vulnerabilities and the paths attackers can use to compromise network resources. The uncertainty about the attacker's behaviour makes Bayesian networks suitable to model attack graphs to perform static and dynamic analysis. Previous approaches have focused on the formalization of attack graphs into a Bayesian model rather than proposing mechanisms for their analysis. In this paper we propose to use efficient algorithms to make exact inference in Bayesian attack graphs, enabling the static and dynamic network risk assessments. To support the validity of our approach we have performed an extensive experimental evaluation on synthetic Bayesian attack graphs with different topologies, showing the computational advantages in terms of time and memory use of the proposed techniques when compared to existing approaches.Comment: 14 pages, 15 figure