We propose an approach to managing the rolling out of a new system type so as to contain the risk of mishaps in its operation to an acceptable level, while using the evidence of safe operation to support confidence for extending the scale of operation. This cautious approach of “bootstrapping” confidence in the safety of a system is now widely applied to autonomous vehicle (AVs), our example. AVs are subject to extreme safety requirements; a major concern is the inability to give meaningful quantitative assurance of safety of an AV type, to the extent required by society, before it is used extensively. We exploit a previously published approach to achieving more moderate, but useful, assurance, e.g. about low enough probability of causing accidents in a limited period of operation; and demonstrate how this approach supports choice of production/deployment strategies, so as to manage the growth of a fleet of AVs for a given accepted level of risk. Via a formal mathematical description of “confidence bootstrapping”, we show: (1) that it is a substantially sound approach in the right circumstances, and useful for deciding about the early deployment phase for a new system; (2) how much confidence can be rightly derived from such a “cautious deployment” approach, avoiding over-optimism; (3) under which conditions our sound formulas for future confidence are applicable; (4) thus, which analyses of the concrete situations, and/or constraints on practice, are needed in order to enjoy the advantages of provably correct confidence in adequate future safety over a definite time (“confidence horizon”).

In recent years we have become interested in the problem of assessing the probability of perfection of software-based systems which are sufficiently simple that they are "possibly perfect". By "perfection" we mean that the software of interest will never fail in a specific operating environment. We can never be certain that it is perfect, so our interest lies in claims for its probability of perfection. Our approach is Bayesian: our aim is to model the changes to this probability of perfection as we see evidence of failure-free working. Much of the paper considers the difficult problem of expressing prior beliefs about the probability of failure on demand (pfd), and representing these mathematically. This requires the assessor to state his prior belief in perfection as a probability, and also to state what he believes are likely values of the pfd in the event that the system is not perfect. We take the view that it will be impractical for an assessor to express these beliefs as a complete distribution for pfd. Our approach to the problem has three threads. Firstly we assume that, although he cannot provide a full probabilistic description of his uncertainty in a single distribution, the assessor can express some precise but partial beliefs about the unknowns. Secondly, we assume that in the inevitable presence of such incompleteness, the Bayesian analysis needs to provide results that are guaranteed to be conservative (because the analyses we have in mind relate to critical systems). Finally, we seek to prune the set of prior distributions that the assessor finds acceptable in order that the conservatism of the results is no greater than it has to be, i.e. we propose, and eliminate, sets of priors that would appear generally unreasonable. We give some illustrative numerical examples of this approach, and note that the numerical values obtained for the posterior probability of perfection in this way seem potentially useful (although we make no claims for the practical realism of the numbers we use). We also note that the general approach here to the problem of expressing and using limited prior belief in a Bayesian analysis may have wider applicability than to the problem we have addressed.

In earlier work, [11] (henceforth LR), an analysis was presented of a 1-out-of-2 software-based system in which one channel was “possibly perfect”. It was shown that, at the aleatory level, the system pfd (probability of failure on demand) could be bounded above by the product of the pfd of channel A and the pnp (probability of nonperfection) of channel B. This result was presented as a way of avoiding the well-known difficulty that for two certainly-fallible channels, failures of the two will be dependent, i.e., the system pfd cannot be expressed simply as a product of the channel pfds. A price paid in this new approach for avoiding the issue of failure dependence is that the result is conservative. Furthermore, a complete analysis requires that account be taken of epistemic uncertainty-here concerning the numeric values of the two parameters pfdAand pnpB. Unfortunately this introduces a different difficult problem of dependence: estimating the dependence between an assessor's beliefs about the parameters. The work reported here avoids this problem by obtaining results that require only an assessor's marginal beliefs about the individual channels, i.e., they do not require knowledge of the dependence between these beliefs. The price paid is further conservatism in the results.

We consider the problem of assessing the reliability of a 1-out-of-2 software-based system, in which failures of the two channels cannot be assumed to be independent with certainty. An informal approach to this problem assesses the channel probabilities of failure on demand (pfds) conservatively, and then multiplies these together in the hope that the conservatism will be sufficient to overcome any possible dependence between the channel failures. Our intention here is to place this kind of reasoning on a formal footing. We introduce a notion of "not worse than independence"' and assume that an assessor has a prior belief about this, expressed as a probability. We obtain a conservative prior system pfd, and show how a conservative posterior system pfd can be obtained following the observation of a number of demands without system failure. We present some illustrative numerical examples, discuss some of the difficulties involved in this way of reasoning, and suggest some avenues of future research.

In recent work, we have argued for a formal treatment of confidence about the claims made in dependability cases for software-based systems. The key idea underlying this work is "the inevitability of uncertainty": It is rarely possible to assert that a claim about safety or reliability is true with certainty. Much of this uncertainty is epistemic in nature, so it seems inevitable that expert judgment will continue to play an important role in dependability cases. Here, we consider a simple case where an expert makes a claim about the probability of failure on demand (pfd) of a subsystem of a wider system and is able to express his confidence about that claim probabilistically. An important, but difficult, problem then is how such subsystem (claim, confidence) pairs can be propagated through a dependability case for a wider system, of which the subsystems are components. An informal way forward is to justify, at high confidence, a strong claim, and then, conservatively, only claim something much weaker: "I'm 99 percent confident that the pfd is less than 10-5, so it's reasonable to be 100 percent confident that it is less than 10-3." These conservative pfds of subsystems can then be propagated simply through the dependability case of the wider system. In this paper, we provide formal support for such reasoning.

In this paper we present empirical results and speculative analysis based on observations collected over a two month period from studies with two high interaction honeynets, deployed in a corporate and an SME (small to medium enterprise) environment, and a distributed honeypots deployment. All three networks contain a mixture of Windows and Linux hosts. We detail the architecture of the deployment and results of comparing the observations from the three environments. We analyze in detail the times between attacks on different hosts, operating systems, networks or geographical location. Even though results from honeynet deployments are reported often in the literature, this paper provides novel results analyzing traffic from three different types of networks and some initial exploratory models. This research aims to contribute to endeavours in the wider security research community to build methods, grounded on strong empirical work, for assessment of the robustness of computer-based systems in hostile environments.

Computer-based advisory systems form with their users composite, human-machine systems. Redundancy and diversity between the human and the machine are often important for the dependability of such systems. We describe a case study on assessing failure probabilities for the analysis of X-ray films for detecting cancer, performed by a person assisted by a computer-based tool. Differently from most approaches to human reliability assessment, we focus on the effects of failure diversity – or correlation – between humans and machines. We illustrate some of the modelling and prediction problems, especially those caused by the presence of the human component. We show two alternative models, with their pros and cons, and illustrate, via numerical examples and analytically, some interesting and non-intuitive answers to questions about reliability assessment and design choices for human-computer systems. Copyright of the authors, 2002 This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 11