Arguing satisfaction of security requirements

This chapter presents a process for security requirements elicitation and analysis, based around the construction of a satisfaction argument for the security of a system. The process starts with the enumeration of security goals based on assets in the system, then uses these goals to derive security requirements in the form of constraints. Next, a satisfaction argument for the system is constructed, using a problem-centered representation, a formal proof to analyze properties that can be demonstrated, and structured informal argumentation of the assumptions exposed during construction of the argument. Constructing the satisfaction argument can expose missing and inconsistent assumptions about system context and behavior that effect security, and a completed argument provides assurances that a system can respect its security requirements

Cognitive science applied to reduce network operation margins

This is a post-peer-review, pre-copyedit version of an article published in Photonic Network Communications. The final authenticated version is available online at: https://doi.org/10.1007/s11107-017-0717-9.In an increasingly competitive market environment with smaller product offer differentiation, a continuous maximization of efficiency, while guarantying the quality of the provided services, remains a main objective for any telecom operator. In this work, we address the reduction of the operational costs of the optical transport network as one of the possible fields of action to achieve this aim. We propose to apply cognitive science for reducing these costs, specifically by reducing operation margins. We base our work on the case-based reasoning technique by proposing several new schemes to reduce the operation margins established during the design and commissioning phases of the optical links power budgets. From the obtained results, we find that our cognitive proposal provides a feasible solution allowing significant savings on transmitted power that can reach a 49%. We show that there is a certain dependency on network conditions, achieving higher efficiency in low loaded networks where improvements can raise up to 53%.Peer ReviewedPostprint (author's final draft

A Rational Analysis of Alternating Search and Reflection Strategies in Problem Solving

In this paper two approaches to problem solving, search and reflection, are discussed, and combined in two models, both based on rational analysis (Anderson, 1990). The first model is a dynamic growth model, which shows that alternating search and reflection is a rational strategy. The second model is a model in ACT-R, which can discover and revise strategies to solve simple problems. Both models exhibit the explore-insight pattern normally attributed to insight problem solving

A reusable knowledge acquisition shell: KASH

KASH (Knowledge Acquisition SHell) is proposed to assist a knowledge engineer by providing a set of utilities for constructing knowledge acquisition sessions based on interviewing techniques. The information elicited from domain experts during the sessions is guided by a question dependency graph (QDG). The QDG defined by the knowledge engineer, consists of a series of control questions about the domain that are used to organize the knowledge of an expert. The content information supplies by the expert, in response to the questions, is represented in the form of a concept map. These maps can be constructed in a top-down or bottom-up manner by the QDG and used by KASH to generate the rules for a large class of expert system domains. Additionally, the concept maps can support the representation of temporal knowledge. The high degree of reusability encountered in the QDG and concept maps can vastly reduce the development times and costs associated with producing intelligent decision aids, training programs, and process control functions

Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care

This paper presents the outcomes from a cognitive engineering project addressing the design problems of computerized monitoring in neonatal intensive care. Cognitive engineering is viewed, in this project, as a symbiosis between cognitive science and design practice. A range of methodologies has been used: interviews with neonatal staff, ward observations and experimental techniques. The results of these investigations are reported, focusing specifically on the differences between junior and senior physicians in their interpretation of monitored physiological data. It was found that the senior doctors made better use of the different knowledge sources available than the junior doctors. The senior doctors were able to identify more relevant physiological patterns and generated more and better inferences than did their junior colleagues. Expertise differences are discussed in the context of previous psychological research in medical expertise. Finally, the paper discusses the potential utility of these outcomes to inform the design of computerized decision support in neonatal intensive care

An intelligent framework for dynamic web services composition in the semantic web

As Web services are being increasingly adopted as the distributed computing technology of choice to securely publish application services beyond the firewall, the importance of composing them to create new, value-added service, is increasing. Thus far, the most successful practical approach to Web services composition, largely endorsed by the industry falls under the static composition category where the service selection and flow management are done a priori and manually. The second approach to web-services composition aspires to achieve more dynamic composition by semantically describing the process model of Web services and thus making it comprehensible to reasoning engines or software agents. The practical implementation of the dynamic composition approach is still in its infancy and many complex problems need to be resolved before it can be adopted outside the research communities. The investigation of automatic discovery and composition of Web services in this thesis resulted in the development of the eXtended Semantic Case Based Reasoner (XSCBR), which utilizes semantic web and AI methodology of Case Based Reasoning (CBR). Our framework uses OWL semantic descriptions extensively for implementing both the matchmaking profiles of the Web services and the components of the CBR engine. In this research, we have introduced the concept of runtime behaviour of services and consideration of that in Web services selection. The runtime behaviour of a service is a result of service execution and how the service will behave under different circumstances, which is difficult to presume prior to service execution. Moreover, we demonstrate that the accuracy of automatic matchmaking of Web services can be further improved by taking into account the adequacy of past matchmaking experiences for the requested task. Our XSCBR framework allows annotating such runtime experiences in terms of storing execution values of non-functional Web services parameters such as availability and response time into a case library. The XSCBR algorithm for matchmaking and discovery considers such stored Web services execution experiences to determine the adequacy of services for a particular task. We further extended our fundamental discovery and matchmaking algorithm to cater for web services composition. An intensive knowledge-based substitution approach was proposed to adapt the candidate service experiences to the requested solution before suggesting more complex and computationally taxing AI-based planning-based transformations. The inconsistency problem that occurs while adapting existing service composition solutions is addressed with a novel methodology based on Constraint Satisfaction Problem (CSP). From the outset, we adopted a pragmatic approach that focused on delivering an automated Web services discovery and composition solution with the minimum possible involvement of all composition participants: the service provider, the requestor and the service composer. The qualitative evaluation of the framework and the composition tools, together with the performance study of the XSCBR framework has verified that we were successful in achieving our goal

Chemical discovery as belief revision

In this paper we describe STAHLp, a system that constructs componential models of chemical substances. STAHLp is a descendent of Zytkow and Simon's (1986) STAHL system, and both use chemical reactions and known componential models in order to construct new chemical models. However, STAHLp employs a more unified and effective strategy for recovering from erroneous inferences, based partly on de Kleer's (1984) assumption-based method of belief revision. This involves recording the underlying source beliefs or premises which lead to each inferred reaction or model. Where Zytkow and Simon's system required multiple methods for detecting errors and recovering from them, STAHLp uses a more powerful representation and additional rules which allow a unified method for error detection and recovery. When given the same initial data, the new system constructs the same historically correct models as STAHL, but it has other capabilities as well. In particular, STAHLp can modify data it has been given if this is necessary to achieve consistent models, and then proceed to construct new models based on the revised data