Detection of Manipulated Pricing in Smart Energy CPS Scheduling

TO RUN:

• make classifies each element in TestingData.txt as 0 (Normal) or 1 (Abnormal) with associated confidence percentage, and the number of Abnormal elements and where they appear, all to stdout.
• make run same as above
• make print same as above but additionally prints results, in appropriate format, to TestingResults.txt.
• make accuracy performs an accuracy test as described in section \ref{knn+}, prints the number of correct classifications out of 10000 (TrainingData.txt) and the average confidence of correct identifications.

Requires:

• make
• git
• LPSolve
• python3
• pip
• numpy
• math
• collections
• sys
• os