コード例 #1
0
    def detectFileAnomalies(passedData, username):
        # Get the trained data set
        trainedData = DatabaseInteractionClass.getFileLearnedData(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)
コード例 #2
0
#integer version of Zach's IP (129.186.251.4) at time of writing test
print DIM.getLatLongFromIP(2176514820)

#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)