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Abstract. Building Automation Systems (BAS) are crucial for monitoring and controlling buildings, ranging from small homes to critical infrastructure, such as airports or military facilities. A major concern in this context is the security of BAS communication protocols and devices. The building automation and control networking protocol (BACnet) is integrated into products of more than 800 vendors worldwide. However, BACnet devices are vulnerable to attacks. We present a novel solution for the two most important BACnet layers, i.e. those independent of the data link layer technology, namely the network and the application layer. We provide the first implementation and evaluation of traffic normalization for BAS traffic. Our proof of concept code is based on the open source software Snort.

Building automation systems (BAS) are interlinked networks of hardware and software, which monitor and control events in the buildings. One of the data communication protocols used in BAS is Building Automation and Control networking protocol (BACnet) which is an internationally adopted ISO standard for the communication between BAS devices. Although BAS focus on providing safety for inhabitants, decreasing the energy consumption of buildings and reducing their operational cost, their security suffers due to the inherent complexity of the modern day systems. The issues such as monitoring of BAS effectively present a significant challenge, i.e., BAS operators generally possess only partial situation awareness. Especially in large and inter-connected buildings, the operators face the challenge of spotting meaningful incidents within large amounts of simultaneously occurring events, causing the anomalies in the BAS network to go unobserved. In this paper, we present the techniques to analyze and visualize the data for several events from BAS devices in a way that determines the potential importance of such unusual events and helps with the building-security decision making. We implemented these techniques as a mobile (Android) based application for displaying application data and as tools to analyze the communication flows using directed graphs

Post-genomic research deals with challenging problems in screening genomes of organisms for particular functions or potential for being the targets of genetic engineering for desirable biological features. 'Phenotyping' of wild type and mutants is a time-consuming and costly effort by many individuals. This article is a preliminary progress report in research on large-scale automation of phenotyping steps (imaging, informatics and data analysis) needed to study plant gene-proteins networks that influence growth and development of plants. Our results undermine the significance of phenotypic traits that are implicit in patterns of dynamics in plant root response to sudden changes of its environmental conditions, such as sudden re-orientation of the root tip against the gravity vector. Including dynamic features besides the common morphological ones has paid off in design of robust and accurate machine learning methods to automate a typical phenotyping scenario, i.e. to distinguish the wild type from the mutants.