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.
California was the first state to legislate a Safe Routes to School (SR2S) program under Assembly Bill 1475 (1999). SR2S funds construction projects that make it safer for children to walk or bicycle to school and encourages a greater number of children to choose these modes of travel for the school commute. The main goal of this project was to assess the long-term impact of program-funded engineering modifications on walking and bicycling levels and safety. Improvements were evaluated with a targeted method to determine the countermeasures that resulted in safety and mode shift. The major results indicated that the safety of pedestrians increased within 250 ft of an infrastructure improvement, such as a sidewalk. There was also evidence of mode shift near improvements. Positive results for safety and mobility, as well as improved data collection for funded programs, should make SR2S programs competitive among other transportation needs.
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