Information security culture : a behaviour compliance conceptual framework

Understanding the complex dynamic and uncertain characteristics of organisational employees who perform authorised or unauthorised information security activities is deemed to be a very important and challenging task. This paper presents a conceptual framework for classifying and organising the characteristics of organisational subjects involved in these information security practices. Our framework expands the traditional Human Behaviour and the Social Environment perspectives used in social work by identifying how knowledge, skills and individual preferences work to influence individual and group practices with respect to information security management. The classification of concepts and characteristics in the framework arises from a review of recent literature and is underpinned by theoretical models that explain these concepts and characteristics. Further, based upon an exploratory study of three case organisations in Saudi Arabia involving extensive interviews with senior managers, department managers, IT managers, information security officers, and IT staff; this article describes observed information security practices and identifies several factors which appear to be particularly important in influencing information security behaviour. These factors include values associated with national and organisational culture and how they manifest in practice, and activities related to information security management.

An effective model of using negative relevance feedback for information filtering

Over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. This paper, proposes an effective model that use negative relevance feedback based on a pattern mining approach to improve extracted features. This study focuses on two main issues of using negative relevance feedback: the selection of constructive negative examples to reduce the space of negative examples; and the revision of existing features based on the selected negative examples. The former selects some offender documents, where offender documents are negative documents that are most likely to be classified in the positive group. The later groups the extracted features into three groups: the positive specific category, general category and negative specific category to easily update the weight. An iterative algorithm is also proposed to implement this approach on RCV1 data collections, and substantial experiments show that the proposed approach achieves encouraging performance.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

The effect of language models on phonetic decoding for spoken term detection

Spoken term detection (STD) popularly involves performing word or sub-word level speech recognition and indexing the result. This work challenges the assumption that improved speech recognition accuracy implies better indexing for STD. Using an index derived from phone lattices, this paper examines the effect of language model selection on the relationship between phone recognition accuracy and STD accuracy. Results suggest that language models usually improve phone recognition accuracy but their inclusion does not always translate to improved STD accuracy. The findings suggest that using phone recognition accuracy to measure the quality of an STD index can be problematic, and highlight the need for an alternative that is more closely aligned with the goals of the specific detection task.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Personalised ontology learning and mining for web information gathering

Over the last decade, the rapid growth and adoption of the World Wide Web has further exacerbated user needs for e±cient mechanisms for information and knowledge location, selection, and retrieval. How to gather useful and meaningful information from the Web becomes challenging to users. The capture of user information needs is key to delivering users' desired information, and user pro¯les can help to capture information needs. However, e®ectively acquiring user pro¯les is di±cult. It is argued that if user background knowledge can be speci¯ed by ontolo- gies, more accurate user pro¯les can be acquired and thus information needs can be captured e®ectively. Web users implicitly possess concept models that are obtained from their experience and education, and use the concept models in information gathering. Prior to this work, much research has attempted to use ontologies to specify user background knowledge and user concept models. However, these works have a drawback in that they cannot move beyond the subsumption of super - and sub-class structure to emphasising the speci¯c se- mantic relations in a single computational model. This has also been a challenge for years in the knowledge engineering community. Thus, using ontologies to represent user concept models and to acquire user pro¯les remains an unsolved problem in personalised Web information gathering and knowledge engineering. In this thesis, an ontology learning and mining model is proposed to acquire user pro¯les for personalised Web information gathering. The proposed compu- tational model emphasises the speci¯c is-a and part-of semantic relations in one computational model. The world knowledge and users' Local Instance Reposito- ries are used to attempt to discover and specify user background knowledge. From a world knowledge base, personalised ontologies are constructed by adopting au- tomatic or semi-automatic techniques to extract user interest concepts, focusing on user information needs. A multidimensional ontology mining method, Speci- ¯city and Exhaustivity, is also introduced in this thesis for analysing the user background knowledge discovered and speci¯ed in user personalised ontologies. The ontology learning and mining model is evaluated by comparing with human- based and state-of-the-art computational models in experiments, using a large, standard data set. The experimental results are promising for evaluation. The proposed ontology learning and mining model in this thesis helps to develop a better understanding of user pro¯le acquisition, thus providing better design of personalised Web information gathering systems. The contributions are increasingly signi¯cant, given both the rapid explosion of Web information in recent years and today's accessibility to the Internet and the full text world.

From conceptual process models to running systems: A holistic approach for the configuration of enterprise system processes

This paper proposes a method which aims at increasing the efficiency of enterprise system implementations. First, we argue that existing process modeling languages that feature different degrees of abstraction for different user groups exist and are used for different purposes which makes it necessary to integrate them. We describe how to do this using the meta models of the involved languages. Second, we argue that an integrated process model based on the integrated meta model needs to be configurable and elaborate on the enabling mechanisms. We introduce a business example using SAP modeling techniques to illustrate the proposed method.

Role of 3D visual conceptualisation of groundwater system models as a management support tool

After the recent prolonged drought conditions in many parts of Australia it is increasingly recognised that many groundwater systems are under stress. Although this is obvious for systems that are utilised for intensive irrigation many other groundwater systems are also impacted.Management strategies are highly variable to non-existent. Policy and regulation are also often inadequate, and are reactive or politically driven. In addition, there is a wide range of opinion by water users and other stakeholders as to what is “reasonable”management practice. These differences are often related to the “value”that is put on the groundwater resource. Opinions vary from “our right to free water”to an awareness that without effective management the resource will be degraded. There is also often misunderstanding of surface water-groundwater linkages, recharge processes, and baseflow to drainage systems.

Groundwater Visualisation System (GVS): a software framework for developing low-end, scalable and robust software for 3D visualisation and animation of groundwater conceptual models

Effective management of groundwater requires stakeholders to have a realistic conceptual understanding of the groundwater systems and hydrological processes.However, groundwater data can be complex, confusing and often difficult for people to comprehend..A powerful way to communicate understanding of groundwater processes, complex subsurface geology and their relationships is through the use of visualisation techniques to create 3D conceptual groundwater models. In addition, the ability to animate, interrogate and interact with 3D models can encourage a higher level of understanding than static images alone. While there are increasing numbers of software tools available for developing and visualising groundwater conceptual models, these packages are often very expensive and are not readily accessible to majority people due to complexity. .The Groundwater Visualisation System (GVS) is a software framework that can be used to develop groundwater visualisation tools aimed specifically at non-technical computer users and those who are not groundwater domain experts. A primary aim of GVS is to provide management support for agencies, and enhancecommunity understanding.

Object-centric process models and the design of flexible processes

Mainstream business process modelling techniques promote a design paradigm wherein the activities to be performed within a case, together with their usual execution order, form the backbone of a process model, on top of which other aspects are anchored. This paradigm, while eective in standardised and production-oriented domains, shows some limitations when confronted with processes where case-by-case variations and exceptions are the norm. In this thesis we develop the idea that the eective design of exible process models calls for an alternative modelling paradigm, one in which process models are modularised along key business objects, rather than along activity decompositions. The research follows a design science method, starting from the formulation of a research problem expressed in terms of requirements, and culminating in a set of artifacts that have been devised to satisfy these requirements. The main contributions of the thesis are: (i) a meta-model for object-centric process modelling incorporating constructs for capturing exible processes; (ii) a transformation from this meta-model to an existing activity-centric process modelling language, namely YAWL, showing the relation between object-centric and activity-centric process modelling approaches; and (iii) a Coloured Petri Net that captures the semantics of the proposed meta-model. The meta-model has been evaluated using a framework consisting of a set of work ow patterns. Moreover, the meta-model has been embodied in a modelling tool that has been used to capture two industrial scenarios.

Community engagement for development and application of 3D visualisation models : a two edged

Background: SEQ Catchments Ltd and QUT are collaborating on groundwater investigations in the SE Qld region, which utilise community engagement and 3D Visualisation methodologies. The projects, which have been funded by the Australian Government’s NHT and Caring for our Country programmes, were initiated from local community concerns regarding groundwater sustainability and quality in areas where little was previously known. ----- Objectives: Engage local and regional stakeholders to tap all available sources of information;•Establish on-going (2 years +) community-based groundwater / surface water monitoring programmes;•Develop 3D Visualisation from all available data; and•Involve, train and inform the local community for improved on-ground land and water use management. ----- Results and findings: Respectful community engagement yielded information, access to numerous monitoring sites and education opportunities at low cost, which would otherwise be unavailable. A Framework for Community-Based Groundwater Monitoring has been documented (Todd, 2008).A 3D visualisation models have been developed for basaltic settings, which relate surface features familiar to the local community with the interpreted sub-surface hydrogeology. Groundwater surface movements have been animated and compared to local rainfall using the time-series monitoring data.An important 3D visualisation feature of particular interest to the community was the interaction between groundwater and surface water. This factor was crucial in raising awareness of potential impacts of land and water use on groundwater and surface water resources.

Communication of business process models via virtual environment simulations

Games and related virtual environments have been a much-hyped area of the entertainment industry. The classic quote is that games are now approaching the size of Hollywood box office sales [1]. Books are now appearing that talk up the influence of games on business [2], and it is one of the key drivers of present hardware development. Some of this 3D technology is now embedded right down at the operating system level via the Windows Presentation Foundations – hit Windows/Tab on your Vista box to find out... In addition to this continued growth in the area of games, there are a number of factors that impact its development in the business community. Firstly, the average age of gamers is approaching the mid thirties. Therefore, a number of people who are in management positions in large enterprises are experienced in using 3D entertainment environments. Secondly, due to the pressure of demand for more computational power in both CPU and Graphical Processing Units (GPUs), your average desktop, any decent laptop, can run a game or virtual environment. In fact, the demonstrations at the end of this paper were developed at the Queensland University of Technology (QUT) on a standard Software Operating Environment, with an Intel Dual Core CPU and basic Intel graphics option. What this means is that the potential exists for the easy uptake of such technology due to 1. a broad range of workers being regularly exposed to 3D virtual environment software via games; 2. present desktop computing power now strong enough to potentially roll out a virtual environment solution across an entire enterprise. We believe such visual simulation environments can have a great impact in the area of business process modeling. Accordingly, in this article we will outline the communication capabilities of such environments, giving fantastic possibilities for business process modeling applications, where enterprises need to create, manage, and improve their business processes, and then communicate their processes to stakeholders, both process and non-process cognizant. The article then concludes with a demonstration of the work we are doing in this area at QUT.

Fusing shrinking and expanding active contour models for robust IRIS segmentation

The performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non- ideal iris images. This paper proposes an improved method to localise non-circular iris images quickly and accurately. Shrinking and expanding active contour methods are consolidated when localising inner and outer iris boundaries. First, the pupil region is roughly estimated based on histogram thresholding and morphological operations. There- after, a shrinking active contour model is used to precisely locate the inner iris boundary. Finally, the estimated inner iris boundary is used as an initial contour for an expanding active contour scheme to find the outer iris boundary. The proposed scheme is robust in finding exact the iris boundaries of non-circular and off-angle irises. In addition, occlusions of the iris images from eyelids and eyelashes are automatically excluded from the detected iris region. Experimental results on CASIA v3.0 iris databases indicate the accuracy of proposed technique.