THE USE OF BAYESIAN BELIEF NETS IN SAFETY ASSESSMENT OF SOFTWARE BASED SYSTEMS

The paper describes an investigation of methods to perform a reliability and safety assessment of the software in programmable safety relevant systems. It emphasises in particular how disparate information sources and different quantitative and qualitative methods should be combined in such an assessment. It starts with a general discussion of rule based, probabilistic and expert judgement methods and their applicability on software reliability. Then a method for combining different evidences in a reliability and safety assessment is pinpointed, viz. the Bayesian Belief Net (BBN) methodology. It is also illustrated how this method may be applied for safety assessment of software.     

贝叶斯网模型的学习、推理和应用

近年来在人工智能领域,不确定性问题一直成为人们关注和研究的焦点。贝叶斯网是用来表示不确定变量集合联合概率分布的图形模式,它反映了变量间潜在的依赖关系。使用贝叶斯网建模已成为解决许多不确定性问题的强有力工具。基于国内外最新的研究成果对贝叶斯网模型的学习、推理和应用情况进行了综述,并对未来的发展方向进行了展望。     

Quantifying security risk level from CVSS estimates of frequency and impact

Modern society relies on and profits from well-balanced computerized systems. Each of these systems has a core mission such as the correct and safe operation of safety critical systems or innovative and effective operation of e-commerce systems. It might be said that the success of these systems depends on their mission. Although the concept of “well-balanced” has a slightly different meaning for each of these two categories of systems, both have to meet customer needs, deliver capabilities and functions according to expectations and generate revenue to sustain today’s highly competitive market. Tighter financial constraints are forcing safety critical systems away from dedicated and expensive communication regimes, such as the ownership and operation of dedicated communication links, towards reliance on third parties and standardized means of communication. As a consequence, knowledge about their internal structures and operations is more widely and publicly available and this can make them more prone to security attacks. These systems are, therefore, moving towards a remotely exploitable environment and the risks associated with this must be controlled.Risk management is a good tool for controlling risk but it has the inherent challenge of quantitatively estimating frequency and impact in an accurate and trustworthy way. Quantifying the frequency and impact of potential security threats requires experience-based data which is limited and rarely reusable because it involves company confidential data. Therefore, there is a need for publicly available data sources that can be used in risk estimation. This paper presents a risk estimation model that makes use of one such data source, the Common Vulnerability Scoring System (CVSS). The CVSS Risk Level Estimation Model estimates a security risk level from vulnerability information as a combination of frequency and impact estimates derived from the CVSS. It is implemented as a Bayesian Belief Network (BBN) topology, which allows not only the use of CVSS-based estimates but also the combination of disparate information sources and, thus, provides the ability to use whatever risk information that is available. The model is demonstrated using a safety- and mission-critical system for drilling operational support, the Measurement and Logging While Drilling (M/LWD) system.     

A participatory Bayesian Belief Network approach to explore ambiguity among stakeholders about socio-ecological systems

Participatory modelling must often deal with the challenge of ambiguity when diverse stakeholders do not share a common understanding of the problem and measures for its solution. In this paper, we propose a framework and a methodology to elicit ambiguities among different stakeholders by using a participatory Bayesian Belief Network (BBN) modelling approach. Our approach consists of four steps undertaken with stakeholders: (1) co-construction of a consensual conceptual model of their socio-ecological system, (2) translation of the model into a consensual Bayesian Net structure, (3) individual parametrization of conditional probabilities, and (4) elicitation of ambiguity through the use of scenarios. We tested this methodology on the ambiguity surrounding the effect of an ecological process on a potential innovation in biological control, and it proved useful in eliciting ambiguity. Further research could explore more conflictual or controversial ambiguities to test this methodology in other settings.     

Mining and visualising ordinal data with non-parametric continuous BBNs

Data mining is the process of extracting and analysing information from large databases. Graphical models are a suitable framework for probabilistic modelling. A Bayesian Belief Net (BBN) is a probabilistic graphical model, which represents joint distributions in an intuitive and efficient way. It encodes the probability density (or mass) function of a set of variables by specifying a number of conditional independence statements in the form of a directed acyclic graph. Specifying the structure of the model is one of the most important design choices in graphical modelling. Notwithstanding their potential, there are only a limited number of applications of graphical models on very complex and large databases. A method for mining ordinal multivariate data using non-parametric BBNs is presented. The main advantage of this method is that it can handle a large number of continuous variables, without making any assumptions about their marginal distributions, in a very fast manner. Once the BBN is learned from data, it can be further used for prediction. This approach allows for rapid conditionalisation, which is a very important feature of a BBN from a user’s standpoint.     

Bayesian Belief Network-based approach for diagnostics and prognostics of semiconductor manufacturing systems

Semiconductor manufacturing is a complex process in that it requires different types of equipments (also referred to as tools in semiconductor industry) with various control variables under monitoring. As the number of sensors grows, a huge amount of data are collected from the production; and yet, the relations among these control variables and their effects on finished wafer are to be fully understood for both equipment monitoring and quality assurance. Meanwhile, as the wafer goes through multiple periods with different recipes, failure that occurs during the process can both cause tremendous loss to manufacturer and compromise product quality. Therefore, occurred failure should be detected as soon as possible, and root cause need to be identified so that corrections can be made in time to avoid further loss. In this paper, we propose to apply Bayesian Belief Network (BBN) to investigate the causal relationship among process variables on the tool and evaluate their influence on wafer quality. By building BBN models at different periods of the process, the causal relation between control parameters, and their influence on wafer can be both qualitatively indicated by the network structure and quantitatively measured by the conditional probabilities in the model. In addition, with the BBN probability propagation, one can diagnose root causes when bad wafer is produced; or predict the wafer quality when abnormal is observed during the process. Our tests on a Chemical Vapor Deposition (CVD) tool show that the BBN model achieves high classification rate for wafer quality, and accurately identifies problematic sensors when bad wafer is found.     

Slip and fall event detection using Bayesian Belief Network

This paper proposes a method to detect slip-only events and fall events based on the motion activity measure and human silhouette shape variations. Here, we also apply the Bayesian Belief Network (BBN) to model the causality of the events before and after the fall and slip-only events. The motion measure is obtained by analyzing the energy of the motion active (MA) area in the integrated spatiotemporal energy (ISTE) map. Unlike the motion history image (MHI), the ISTE map can be applied to detect fall and slip-only events. The contributions of this study are: (a) proposing the ISTE map; (b) detecting the fall parallel to the optical axis; (c) application to non-fixed frame rate video; (d) identifying the slip-only event; and (e) using BBN to model the causality of the slip or fall events with other events. Early identification of a slip-only event can help prevent falls and injuries. In the experiments, we demonstrate that our method is effective in detecting both fall and slip-only events.     

基于用户需求的软件项目风险管理模型

控制软件项目的风险是软件项目管理的重要组成部分。目前的软件风险管理方法存在着一些不足,在软件项目管理实践中不能取得最佳效果。文章通过对软件产品开发中资源、用户需求和产品之间的内在关系的分析,提出了基于用户需求的软件项目风险管理模型,该模型从用户需求角度出发,通过软件过程技术、产品工程技术和度量技术的支持可以有效地控制软件项目风险,保证了软件产品满足用户需求的能力,从而使软件项目达到成功。在模型的基础上,文章对实现模型的技术进行了研究,给出了模型的BayesianBeliefNetworks实现方法。     

Adaptive modelling for adaptive water quality management in the Great Barrier Reef region,Australia

The development and use of a Bayesian Belief Network (BBN) model, within an adaptive management process for the management of water quality in the Mackay Whitsunday region of Queensland, Australia is described. The management goal is firstly to set achievable targets for water quality entering the Great Barrier Reef lagoon from the Mackay Whitsunday natural resource management region and then secondly to define and implement a strategy to achieve these targets. The BBN serves as an adaptive framework that managers and scientists may use to articulate what they know about the managed system. It then provides a tool to guide where, when and what interventions (including research) are most likely to achieve management outcomes. Importantly the BBN provides a platform for collective learning.BBN estimates of total suspended sediment (TSS) loads and event mean concentrations (EMCs) were compared to observed data and results from current best practice models. The BBN estimates were reasonable relative to empirical observations. Example results from the BBN are thereafter used to illustrate the use of the model in estimating the likelihood of exceeding water quality targets with and without proposed actions to improve water quality. Example results are also used to illustrate what spatial or land use elements might contribute most to exceeding water quality targets. Finally key limitations of the tool are discussed and important learnings from the process are highlighted.     

A Bayesian support tool for morphological design

Dynamic computer based support tools for the conceptual design phase have provided a long-standing challenge to develop. This is largely due to the ‘fluid’ nature of the conceptual design phase. Design evaluation methods, which form the basis of most computer design support tools, provide poor support for multiple outcomes. This research proposes a stochastic-based support tool that addresses this problem. A Bayesian Belief Network (BBN) is used to represent the causal links between design variables. Included in this research is an efficient method for learning a design domain network from previous design data in the structure of a morphological design chart. This induction algorithm is based on information content. A user interface is proposed to support dynamically searching the conceptual design space, based on a partial design specification. This support tool is empirically compared against a more traditional search process. While no compelling evidence is produced to support the stochastic-based approach, an interesting broader design search behaviour emerges from observations of the use of the stochastic design support tool.     

Knowledge centered assessment pattern: an effective tool for assessing safety concerns in software architecture

In software-based systems, the notion of software failure is magnified if the software in question is a component of a safety critical system. Hence, to ensure a required level of safety, the product must undergo expensive rigorous testing and verification/validation activities. To minimize the cost of quality (COQ) associated with the development of safety critical systems, it becomes imperative that the assessment of intermediate artifacts (e.g., requirement, design documents or models) is done efficiently and effectively to maximize early defect detection and/or defect prevention. However, as a human-centered process, the assessment of software architecture for safety critical systems relies heavily on the experience and knowledge of the assessment team to ensure that the proposed architecture is consistent with the software functional and safety requirements.The knowledge centered assessment pattern (KCAP) acts as effective tool to assist assessment teams by providing key information on what architectural elements should be assessed, why they should to be assessed, and how they should be assessed. Furthermore, the use of KCAP highlights cases where the software architecture has been properly, over, under, or incoherently engineered.     

A New Safety Assessment Method Based on Belief Rule Base With Attribute Reliability

Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.      

An integrated modelling tool to evaluate the acceptability of irrigation constraint measures for groundwater protection

In many arid and semi-arid regions agriculture is the main user of GW, causing problems with the quantity and quality of water, but there are few institutional policies and regulations governing sustainable GW exploitation. The authors suggest an integrated methodology for enabling local GW management, capable of combining the need for GW protection with socio-economic and behavioural determinants of GW use. In the proposed tool, integration is reinforced by the inclusion of multiple stakeholders, and the use of Bayesian Belief Networks (BBN) to simulate and explore these stakeholders' attitude to GW exploitation and their responses to the introduction of new protection policies. BBNs and hydrological system properties are integrated in a GIS-based decision support system – GeSAP – which can elaborate and analyse scenarios concerning the pressure on GW due to exploitation for irrigation, and the effectiveness of protection policies, taking into account the level of consensus. In addition, the GIS interface makes it possible to spatialize the information and to investigate model results.The paper presents the results of an experimental application of the GeSAP tool to support GW planning and management in the Apulia Region (Southern Italy). To evaluate the actual usability of the GeSAP tool, case study applications were performed involving the main experts in GW protection and the regional decision-makers. Results showed that GeSAP can simulate farmers' behaviour concerning the selection of water sources for irrigation, allowing evaluation of the effectiveness of a wide range of strategies which impact water demand and consumption.     

An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination

An integrated methodology, based on Bayesian belief network (BBN) and evolutionary multi-objective optimization (EMO), is proposed for combining available evidence to help water managers evaluate implications, including costs and benefits of alternative actions, and suggest best decision pathways under uncertainty. A Bayesian belief network is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. In complex applications where the task of defining the network could be difficult, the proposed methodology can be used in validation of the network structure and the parameters of the probabilistic relationship. Furthermore, in decision problems where it is difficult to choose appropriate combinations of interventions, the states of key variables under the full range of management options cannot be analyzed using a Bayesian belief network alone as a decision support tool. The proposed optimization method is used to deal with complexity in learning about actions and probabilities and also to perform inference. The optimization algorithm generates the state variable values which are fed into the Bayesian belief network. It is possible then to calculate the probabilities for all nodes in the network (belief propagation). Once the probabilities of all the linked nodes have been updated, the objective function values are returned to the optimization tool and the process is repeated. The proposed integrated methodology can help in dealing with uncertainties in decision making pertaining to human behavior. It also eliminates the shortcoming of Bayesian belief networks in introducing boundary constraints on probability of state values of the variables. The effectiveness of the proposed methodology is examined in optimum management of groundwater contamination risks for a well field capture zone outside Copenhagen city.     

基于BBN的建设项目进度风险定量分析方法

传统的CPM和PERT方法难以对项目的进度延迟风险进行准确的定量分析。将专家先验知识与问卷调查数据相结合,建立了建设项目进度风险评估的贝叶斯信念网络模型,采用NETICA软件对样本数据进行拟合,得到了网络模型各节点间的条件概率分布。模型的应用证明该方法能够比较准确地实现对进度延迟风险的定量预测,具有良好的应用前景。     

A method of sensor fault detection and identification

A method of Bayesian belief network (BBN)-based sensor fault detection and identification is presented. It is applicable to processes operating in transient or at steady-state. A single-sensor BBN model with adaptable nodes is used to handle cases in which process is in transient. The single-sensor BBN model is used as a building block to develop a multi-stage BBN model for all sensors in the process under consideration. In the context of BBN, conditional probability data represents correlation between process measurable variables. For a multi-stage BBN model, the conditional probability data should be available at each time instant during transient periods. This requires generating and processing a massive data bank that reduces computational efficiency. This paper presents a method that reduces the size of the required conditional probability data to one set. The method improves the computational efficiency without sacrificing detection and identification effectiveness. It is applicable to model- and data-driven techniques of generating conditional probability data. Therefore, there is no limitation on the source of process information. Through real-time operation and simulation of two processes, the application and performance of the proposed BBN method are shown. Detection and identification of different sensor fault types (bias, drift and noise) are presented. For one process, a first-principles model is used to generate the conditional probability data, while for the other, real-time process data (measurements) are used.     

Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey

This paper presents an effort to induce a Bayesian belief network (BBN) from crime data, namely the national crime victimization survey (NCVS). This BBN defines a joint probability distribution over a set of variables that were employed to record a set of crime incidents, with particular focus on characteristics of the victim. The goals are to generate a BBN to capture how characteristics of crime incidents are related to one another, and to make this information available to domain specialists. The novelty associated with the study reported in this paper lies in the use of a Bayesian network to represent a complex data set to non-experts in a way that facilitates automated analysis. Validation of the BBN’s ability to approximate the joint probability distribution over the set of variables entailed in the NCVS data set is accomplished through a variety of sources including mathematical techniques and human experts for appropriate triangulation. Validation results indicate that the BBN induced from the NCVS data set is a good joint probability model for the set of attributes in the domain, and accordingly can serve as an effective query tool.

基于Bayesian网络的软件开发模型

在这篇论文中,阐述了用贝叶斯信任网络(Bayesian Belief Networks:BBN)进行软件建模的方法,提出了基于BBN软件开发模型,该模型能够表示软件过程的主要活动,给出了如何构建BBN开发模型的步骤,在定义要求控制和计划的关键工作流时该模型能支持专家意见,这种模型能够应对软件开发过程的迭代特性,并对开发过程中的每一步都会渐近产生精确评估,根据其结构可对每一个工作流的整体结果做出评估。