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)