def create_graph():
"""Create a graph which prints hello for each even number x in the input stream,
using a conditional RuleBasedModel node and a HelloPrinter h1.Action."""
graph = h1.Graph()
graph.start()\
.add(h1.Decision(RuleBasedModel(), result_field="predictions"))\
.add(yes=HelloPrinter(), no=h1.NoOp())
graph.end()
return graph
def create_autocyber_graph():
graph = h1.Graph()
graph.start()\
.add(WindowGenerator())\
.add(h1.Decision(MsgFreqEventDetectorModel().load(), decision_field="WindowInAttack"))\
.add(yes=GradientBoostingMsgClassifierModel().load(),
no=NoOp())
graph.end()
return graph
m2.evaluate(prepared_data)
m2.metrics['confusion_matrix']
from AutomotiveCybersecurity.graph import WindowGenerator
class NoOp(h1.Action):
def call(self, command, inputs):
pass
graph = h1.Graph()
graph.start()\
.add(WindowGenerator())\
.add(h1.Decision(m, decision_field="WindowInAttack"))\
.add(yes=m2,
no=NoOp())
graph.end()
df = pd.read_csv(data['test_attack_files'][0])
df.columns = [
'Timestamp',
'Label',
'CarSpeed',
'SteeringAngle',
'YawRate',
'Gx',
'Gy',
]
prepared_data = m2.prep_data(data)
m2.train(prepared_data)
m2.evaluate(prepared_data)
m2.persist()
class NoOp(h1.Action):
def call(self, command, inputs):
pass
graph = h1.Graph()
graph.start()\
.add(WindowGenerator())\
.add(h1.Decision(MsgFreqEventDetectorModel().load(), decision_field="WindowInAttack"))\
.add(yes=GradientBoostingMsgClassifierModel().load(),
no=NoOp())
graph.end()
results = graph.predict({"df": df})
results.keys()
Now let's evaluate the whole graph, especially focusing on the event-level TPR & FPR since they are crucial in the safe-mode deployment use case.
from AutomotiveCybersecurity.util import evaluate_event_graph
evaluate_event_graph(graph, data['test_attack_files'])
Woa! We ran through all 400ms windows in the test samples and got event-level FPR=0.0% with zero false positives! (Note that this is still a subsample of the data, but once you've tried it on the full dataset the results should be the same: zero false positive at event-level.)