def test_model_one_stroke(self):
print("Testing by training model with one keystroke")
user = self.kda.getUser(TestAuthenticator.validUser[0])
for functionName in TestAuthenticator.validFunction:
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
self.assertEqual(detector.evaluate(user.getStrokes(1)[0]), 1)
def test_invalid_type_population(self):
print("Test spoofing using a nonsensical string ")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
with self.assertRaises(TypeError):
spoofer = KeystrokeSpoofer("Something between 2 and 7 ", detector)
spoofer.createSpoof()
def test_floating_population(self):
print("Test spoofing using a fraction as a population ")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
with self.assertWarns(UserWarning):
spoofer = KeystrokeSpoofer(20.3423, detector)
spoofer.createSpoof()
def test_zero_population(self):
print("Testing by trying to setting population to zero")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
with self.assertRaises(ValueError):
spoofer = KeystrokeSpoofer(0, detector)
spoofer.createSpoof()
def test_positive_population(self):
print("Testing model using a valid population")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
print(user.getStrokes(1))
spoofer = KeystrokeSpoofer(100, detector)
self.assertTrue(len(spoofer.createSpoof()) > 0)
def test_keystroke_similarity(self):
print("Testing similarity rating on evaluators")
user = self.kda.getUser(TestAuthenticator.validUser[0])
fakeKeystroke = np.full(31, 50)
for functionName in TestAuthenticator.validFunction:
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getTrainingVector())
# Fake should be less similar then the real user keystroke
self.assertTrue(
detector.evaluate(fakeKeystroke) < detector.evaluate(
user.getStrokes(1)[0]))
def setUp(self):
self.kda = KeystrokeDynamicAttacker(
"Resources/DSL-StrongPasswordData.csv")
class TestAuthenticator(unittest.TestCase):
validFunction = [
"Euclidean", "Euclidean normed", "Manhattan", "Manhattan scaled"
]
invalidFunction = "I am not a function"
validUser = ["s022", "s033"]
invalidUser = "******"
def setUp(self):
self.kda = KeystrokeDynamicAttacker(
"Resources/DSL-StrongPasswordData.csv")
def test_keystroke_similarity(self):
print("Testing similarity rating on evaluators")
user = self.kda.getUser(TestAuthenticator.validUser[0])
fakeKeystroke = np.full(31, 50)
for functionName in TestAuthenticator.validFunction:
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getTrainingVector())
# Fake should be less similar then the real user keystroke
self.assertTrue(
detector.evaluate(fakeKeystroke) < detector.evaluate(
user.getStrokes(1)[0]))
# test the model using one keystroke as it should give us back a perfect match
def test_model_one_stroke(self):
print("Testing by training model with one keystroke")
user = self.kda.getUser(TestAuthenticator.validUser[0])
for functionName in TestAuthenticator.validFunction:
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
self.assertEqual(detector.evaluate(user.getStrokes(1)[0]), 1)
# Test positive value as population
def test_positive_population(self):
print("Testing model using a valid population")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
print(user.getStrokes(1))
spoofer = KeystrokeSpoofer(100, detector)
self.assertTrue(len(spoofer.createSpoof()) > 0)
# Test positive value as population
def test_negative_population(self):
print("Testing by trying to set a negative population")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
with self.assertRaises(ValueError):
spoofer = KeystrokeSpoofer(-100, detector)
spoofer.createSpoof()
# Test with 0 as a population
def test_zero_population(self):
print("Testing by trying to setting population to zero")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
with self.assertRaises(ValueError):
spoofer = KeystrokeSpoofer(0, detector)
spoofer.createSpoof()
def test_floating_population(self):
print("Test spoofing using a fraction as a population ")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
with self.assertWarns(UserWarning):
spoofer = KeystrokeSpoofer(20.3423, detector)
spoofer.createSpoof()
def test_invalid_type_population(self):
print("Test spoofing using a nonsensical string ")
user = self.kda.getUser(TestAuthenticator.validUser[0])
functionName = TestAuthenticator.validFunction[0]
detector = KeystrokeDynamicAttacker.getDetector(functionName)
detector.trainModel(user.getStrokes(1))
with self.assertRaises(TypeError):
spoofer = KeystrokeSpoofer("Something between 2 and 7 ", detector)
spoofer.createSpoof()
def test_valid_keystroke_getter(self):
print("Test to get make sure keystroke getter works ")
user = self.kda.getUser(TestAuthenticator.validUser[0])
self.assertEqual(len(user.getStrokes(10)), 10)
def test_negative_keystroke_getter(self):
print("Try getting back negative keystrokes ")
user = self.kda.getUser(TestAuthenticator.validUser[0])
with self.assertWarns(UserWarning):
self.assertEqual(len(user.getStrokes(-100)), 0)
def test_out_of_bound_keystroke_getter(self):
print("Try getting more keystrokes then is available ")
user = self.kda.getUser(TestAuthenticator.validUser[0])
with self.assertWarns(UserWarning):
self.assertEqual(len(user.getStrokes(1000)), 400)
def test_making_children(self):
print("Seeing the result of making kids ")
# make an ultra-fit parent with [0, 0, 0] as keystroke
parent1 = Keystroke(np.zeros(3), 1)
# make an ultra-fit parent with [5, 5, 5] as keystroke
parent2 = Keystroke(np.full(3, 5), 1)
perfectKid = np.full(3, 5)
# 10000 is being dramatic because there is a 5% chance of mutation
# But, with perfects who are perfectly fit we should have no mutation
for i in range(10000):
self.assertTrue((parent1.makeChild(parent2) - perfectKid).all())
self.assertTrue((parent2.makeChild(parent1) - perfectKid).all())
def test_invalid_user_main(self):
print("Test main with an invalid user ")
with self.assertRaises(ValueError):
self.kda.spoofUser(TestAuthenticator.invalidUser,
TestAuthenticator.validFunction[0], 20)
def test_invalid_spoof_function_main(self):
print("Test main with invalid function for evaluation ")
with self.assertRaises(ValueError):
self.kda.spoofUser(TestAuthenticator.validUser[0],
TestAuthenticator.invalidFunction, 20)
def test_invalid_classify_function_main(self):
print("Test main with invalid function for evaluation ")
with self.assertRaises(ValueError):
self.kda.classifyUsers(TestAuthenticator.invalidFunction)
# test detection of obvious goats
def testGoat(self):
print("Test if classifier can detect goats amidst group of sheep")
sheep = AnimalCreator.create_sheep(200)
goat = AnimalCreator.create_goat(2)
# add the goats to the sheep
sheep.update(goat)
detector = self.kda.getDetector("Manhattan")
classifier = Classifier(sheep, detector, True)
sheepIDs, lambsIDs, goatsIDs, wolvesIDs = classifier.classifyUser()
self.assertEqual(sheepIDs, 200)
# self.assertEqual(lambsIDs, 0) # can't guarantee
self.assertEqual(goatsIDs, 2)
# self.assertEqual(wolvesIDs, 0)
# test detection of obvious wolf and lambs
def test_wolf_and_lamb(self):
print("Test if classifier can detect lambs and wolves")
sheep = AnimalCreator.create_sheep(400)
animal = AnimalCreator.create_lambs_and_wolves(2)
goat = AnimalCreator.create_goat(5)
# add the animals to the sheep
animal.update(sheep)
animal.update(goat)
detector = self.kda.getDetector("Manhattan")
classifier = Classifier(animal, detector, True)
sheepIDs, lambsIDs, goatsIDs, wolvesIDs = classifier.classifyUser()
self.assertTrue(400 <= sheepIDs <= 406)
self.assertEqual(lambsIDs, 4)
self.assertEqual(goatsIDs, 5)
self.assertEqual(wolvesIDs, 2)