This paper will show how the accuracy and security of SCADA systems can be improved by using anomaly detection to identify bad values caused by attacks and faults. The performance of invariant induction and ngram anomaly-detectors will be compared and this paper will also outline plans for taking this work further by integrating the output from several anomalydetecting techniques using Bayesian networks. Although the methods outlined in this paper are illustrated using the data from an electricity network, this research springs from a more general attempt to improve the security and dependability of SCADA systems using anomaly detection.
Scientists studying consciousness are attempting to identify correlations between measurements of consciousness and the physical world. Consciousness can only be measured through first-person reports, which raises problems about the accuracy of first-person reports, the possibility of non-reportable consciousness and the causal closure of the physical world. Many of these issues could be resolved by assuming that consciousness is entirely physical or functional. However, this would sacrifice the theory-neutrality that is a key attraction of a correlates-based approach to the study of consciousness. This paper puts forward a different solution that uses a framework of definitions and assumptions to explain how consciousness can be measured. This addresses the problems associated with first-person reports and avoids the issues with the causal closure of the physical world. This framework is compatible with most of the current theories of consciousness and it leads to a distinction between two types of correlates of consciousness.
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