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)
#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)