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At least 559 records · Page 31

Rapid Threat Organism Recognition Pipeline

The RAPTOR computational pipeline identifies microbial nucleic acid sequences present in sequence data from clinical samples. It takes as input raw short-read genomic sequence data (in particular, the type generated by the Illumina sequencing platforms) and outputs taxonomic evaluation of detected microbes in various human-readable formats. This software was designed to assist in the diagnosis or characterization of infectious disease, by detecting pathogen sequences in nucleic acid sequence data from clinical samples. It has also been applied in the detection of algal pathogens, when algal biofuel ponds became unproductive. RAPTOR first trims and filters genomic sequence reads based on quality and related considerations, then performs a quick alignment to the human (or other host) genome to filter out host sequences, then performs a deeper search against microbial genomes. Alignment to a protein sequence database is optional. Alignment results are summarized and placed in a taxonomic framework using the Lowest Common Ancestor algorithm.

Williams, Kelly P.↗

A Systems Approach to Analyzing Cyber-Physical Threats in the Smart Grid

This paper presents a systems analysis approach to characterizing the risk of a Smart Grid to a load-drop attack. A characterization of the risk is necessary for the design of detection and remediation strategies to address the consequences of such attacks. Using concepts from systems health management and system engineering, this work (a) first identifies metrics that can be used to generate constraints for security features, and (b) lays out an end-to-end integrated methodology using separate network and power simulations to assess system risk. We demonstrate our approach by performing a systems-style analysis of a load-drop attack implemented over the AMI subsystem and targeted at destabilizing the underlying power grid.

Almajali, Anas↗