Behaviour Engines allow the acquirement of tacit (implicit or none verbalists) knowledge by using an acquire-by-action workflow and provide a direct interaction platform between the domain expert and the evolving project code based on an intuitive justification-conclusion language, thus surpassing legacy policy engines by being a self developing and learning mechanism. This paper seeks to formulate the current state of the art in technology and processes and attempts to merge the application of ontological decision techniques of behaviour engines with network packet capture data, to detect data exfiltration attempts over covert channelling. The final goal of the research will be to develop a behaviour engine/intrusion detection solution for pre-emptive counter-measures to anomalous behaviour from within or without a network.

In this paper we present an architecture which enables data-sharing between computer security and network traffic visualisation tools. At its core is a middleware which manages all interactions. This enables one application to determine the acceptable input for another, and send compatible data. The middleware sits atop a unified database which provides data in a generic form to the applications. Interesting traffic patterns or attack trends seen in one tool can be sent directly to another for further examination and analysis. All communication in the middleware is performed using XML as a data transport mechanism.

We demonstrate a prototype toolkit that uses constraints and constraint violation repair techniques to enable the automated detection and, where possible, the automated resolution of conflicts in taxonomic databases.

The LITCHI project is concerned with the integration and maintenance of databases of biological knowledge organised by species. We use constraints pertaining to good taxonomic practice in order to identify taxonomix conflicts in individual species databases and in databases formed by merging species databases from distinct sources. The LITCHI system can be used to resolve such conflicts incrementally. As the project has progressed, we have identified a number of distinctive features of the problem domain, and needs of the intended users, which have had a significant impact on the techniques and modes of operation that we found to be appropriate, especially in contrast with applications that handle rapidly-accumulating 'raw' data. It is upon these aspects of LITCHI that we concentrate in the present paper viewing LITCHI as an example of the more general problem of merging scientific data sets in which conflicts between the terminology used can occur.

Over recent years, international initiatives such as the 1993 UN Convention on Biological Diversity have highlighted the need for information about species diversity on a global scale. However, attempts to build global information systems by integrating smaller, independently created biodiversity databases have been hampered by differences in the sets of species names used. Some databases use different names to refer to the same species, while in other cases the same name can be applied to differing definitions of a species, or even entirely different species. The LITCHI project aims to assist biologists in the integration of databases by searching for conflicts within taxonomic checklists (i.e. lists of the species names used in a database and the relationships between them). In order to detect such conflicts, we have created a formal model of taxonomic practice, which describes (amongst other things) what it means for a checklist to be consistent and well-specified. This model has been used as the basis for a prototype tool that uses Prolog to search for naming conflicts within a relational database of checklists. We describe the background to our formal model and show how it has been used to implement the LITCHI system. Our prototype tool is already proving its worth by detecting conflicts and errors within real taxonomic checklists.

Summary form only given. The LITCHI project (Logic-based Integration of Taxonomic Conflicts in Heterogeneous Information Systems) aims to develop software to enable the automated detection and, where possible, resolution of conflicts in taxonomic checklists. A taxonomic checklist is a list of the names of species (and other taxa) used within a particular biological database. Since species names are typically used to gain access to data within biological databases, checklists provide a concise representation of the data values that can act as keys when querying such databases. More importantly, species names are also typically used as the join attribute when integrating several biological databases. However, naming of species is a subjective activity, and different scientific communities will have different ideas about the names that should be used for particular species. These conflicts of opinion arise as a result of the subjective nature of the classification process and geographical or historical differences in background knowledge. Some communities may use different names for the same species, while other groups of scientists may use the same name to refer to different species. Often, there is no one right naming scheme, but some consistent set of names must be used if biological databases are to be integrated. Therefore, there is a real need for a tool which will assist biologists in the integration of checklists, prior to the integration of species databases, so that these differences of opinion can be resolved.