Intrusion detection in computer networks faces the problem of a large number of both false alarms and unrecognized attacks. To improve the precision of detection, various machine learning techniques have been proposed. However, one critical issue is that the amount of reference data that contains serious intrusions is very sparse. In this paper we present an inference process with linear chain conditional random fields that aims to solve this problem by using domain knowledge about the alerts of different intrusion sensors represented in an ontology.

In this paper we survey the architecture and AI aspects in our project on early warningand intrusion detection based on combined AI methods. We address the problem of alarm assessment in intrusion detection and use plan reconstruction based on hierarchically organised procedural knowledge that contains descriptions of adversary actions. Reconstructed plans are supposed to correlate events and alarms from a SIEM and provide explanations for a security expert. We also aim at predicting the next steps of multi-stage intrusion attacks in computer networks. Therefore a probabilistic relational reasoning over time method based on hidden Markov

In this paper, we describe an approach to generate semantic descriptions of entities in city maps so that they can be presented through accessible interfaces. The solution we present processes bitmap images containing city map excerpts. Regions of interest in these images are extracted automatically based on colour information and subsequently their geometric properties are determined. The result of this process is a structured description of these regions based on the Geography Markup Language (GML), an XML based format for the description of GIS data. This description can later serve as an input to innovative presentations of spatial structures using haptic and auditory interfaces.

The EU-supported TeDUB (Technical Drawings Understanding for the Blind) project is developing a software system that aims to make technical diagrams accessible to blind and visually impaired people. It consists of two separate modules: one that analyses drawings either semi-automatically or automatically, and one that presents the results of this analysis to blind people and allows them to interact with it. The system is capable of analysing and presenting diagrams from a number of formally defined domains. A diagram enters the system as one of two types: first, diagrams contained in bitmap images, which do not explicitly contain the semantic structure of their content and thus have to be interpreted by the system, and second, diagrams obtained in a semantically enriched format that already yields this structure. The TeDUB system provides blind users with an interface to navigate and annotate these diagrams using a number of input and output devices. Extensive user evaluations have been carried out and an overall positive response from the participants has shown the effectiveness of the approach.

The overall aim of this work is to grant blind users access to graphically represented information. In order to enable them to also search and retrieve this information an RDF(S) representation is shown which further leads to an application which enables another tininess of the semantic web by extracting explicit semantics of line drawing images.