def detectNetworkAnomalies(passedData, username):
# Get the trained data set
trainedData = DatabaseInteractionClass.getNetworkLearnedData(username)
npTrainedData = np.array(trainedData)
# Generate the model, and fit the trained data to it
clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.00005)
clf.fit(npTrainedData)
# This should return an array of 1's and -1's (in float64 form), the -1's corresponding to failures
return clf.predict(passedData)
#integer version of Google's IP address for DNS server (8.8.8.8)
print DIM.getLatLongFromIP((8 * 256 * 256 * 256) + (8 * 256 * 256) + (8 * 256) + 8)
print DIM.setScore("zlbales", 99, 10)
print DIM.setScore("zlbales", 98, 20)
print DIM.setScore("zlbales", 97, 30)
print DIM.setScore("zlbales", 100, 0)
print DIM.getProcessLearnedData("zlbales")
print DIM.getProcessLearnedData("cbcullen")
print DIM.getProcessLearnedData("unknown")
print DIM.getFileLearnedData("zlbales")
print DIM.getFileLearnedData("cbcullen")
print DIM.getFileLearnedData("unknown")
print DIM.getNetworkLearnedData("zlbales")
print DIM.getNetworkLearnedData("cbcullen")
print DIM.getNetworkLearnedData("unknown")
vector = []
for i in range(1,88):
vector.append(i)
print DIM.insertFileLearningData("zlbales",-1,vector)
vector = [1,2,3,4]
print DIM.insertNetworkLearningData("zlbales",-1,vector)
vector = [1,2]
print DIM.insertProcessLearningData("zlbales","test_value.exe",-1,vector)
