def generateChainedStatesAutomata(abstractSession, symbolList):
"""Generate an automata that contains as many states and
transitions as the number of request-response couples in the
abstract session. This automata has thus the shape of a uniq
chain.
>>> from netzob.all import *
>>> symbolSYN = Symbol([Field(ASCII("SYN"))], name="Symbol_SYN")
>>> symbolSYNACK = Symbol([Field(ASCII("SYN/ACK"))], name="Symbol_SYNACK")
>>> symbolACK = Symbol([Field(ASCII("ACK"))], name="Symbol_ACK")
>>> symbolPUSH = Symbol([Field(ASCII("PUSH"))], name="Symbol_PUSH")
>>> symbolList = [symbolSYN, symbolSYNACK, symbolACK, symbolPUSH]
>>> msg1 = RawMessage("SYN", source="A", destination="B")
>>> msg2 = RawMessage("SYN/ACK", source="B", destination="A")
>>> msg3 = RawMessage("ACK", source="A", destination="B")
>>> msg4 = RawMessage("PUSH", source="B", destination="A")
>>> session = Session([msg1, msg2, msg3, msg4])
>>> abstractSession = session.abstract(symbolList)
>>> automata = Automata.generateChainedStatesAutomata(abstractSession, symbolList)
>>> dotcode = automata.generateDotCode()
>>> print(len(dotcode))
1024
>>> print(dotcode) #doctest: +ELLIPSIS
digraph G {
"Start state" [shape=doubleoctagon, style=filled, fillcolor=white, URL="..."];
"State 1" [shape=ellipse, style=filled, fillcolor=white, URL="..."];
"State 2" [shape=ellipse, style=filled, fillcolor=white, URL="..."];
"State 3" [shape=ellipse, style=filled, fillcolor=white, URL="..."];
"End state" [shape=ellipse, style=filled, fillcolor=white, URL="..."];
"Start state" ... "State 1" [fontsize=5, label="OpenChannelTransition", URL="..."];
"State 1" ... "State 2" [fontsize=5, label="Transition (Symbol_SYN;{Symbol_SYNACK})", URL="..."];
"State 2" ... "State 3" [fontsize=5, label="Transition (Symbol_ACK;{Symbol_PUSH})", URL="..."];
"State 3" ... "End state" [fontsize=5, label="CloseChannelTransition", URL="..."];
}
:return: an automata with one sequence of chained states.
:rtype: a :class:`netzob.Model.Grammar.Automata.Automata`
"""
return ChainedStatesAutomataFactory.generate(abstractSession,
symbolList)
def generateChainedStatesAutomata(abstractSession, symbolList):
"""Generate an automata that contains as many states and
transitions as the number of request-response couples in the
abstract session. This automata has thus the shape of a uniq
chain.
>>> from netzob.all import *
>>> symbolSYN = Symbol([Field(ASCII("SYN"))], name="Symbol_SYN")
>>> symbolSYNACK = Symbol([Field(ASCII("SYN/ACK"))], name="Symbol_SYNACK")
>>> symbolACK = Symbol([Field(ASCII("ACK"))], name="Symbol_ACK")
>>> symbolPUSH = Symbol([Field(ASCII("PUSH"))], name="Symbol_PUSH")
>>> symbolList = [symbolSYN, symbolSYNACK, symbolACK, symbolPUSH]
>>> msg1 = RawMessage("SYN", source="A", destination="B")
>>> msg2 = RawMessage("SYN/ACK", source="B", destination="A")
>>> msg3 = RawMessage("ACK", source="A", destination="B")
>>> msg4 = RawMessage("PUSH", source="B", destination="A")
>>> session = Session([msg1, msg2, msg3, msg4])
>>> abstractSession = session.abstract(symbolList)
>>> automata = Automata.generateChainedStatesAutomata(abstractSession, symbolList)
>>> dotcode = automata.generateDotCode()
>>> print(len(dotcode))
1024
>>> print(dotcode) #doctest: +ELLIPSIS
digraph G {
"Start state" [shape=doubleoctagon, style=filled, fillcolor=white, URL="..."];
"State 1" [shape=ellipse, style=filled, fillcolor=white, URL="..."];
"State 2" [shape=ellipse, style=filled, fillcolor=white, URL="..."];
"State 3" [shape=ellipse, style=filled, fillcolor=white, URL="..."];
"End state" [shape=ellipse, style=filled, fillcolor=white, URL="..."];
"Start state" ... "State 1" [fontsize=5, label="OpenChannelTransition", URL="..."];
"State 1" ... "State 2" [fontsize=5, label="Transition (Symbol_SYN;{Symbol_SYNACK})", URL="..."];
"State 2" ... "State 3" [fontsize=5, label="Transition (Symbol_ACK;{Symbol_PUSH})", URL="..."];
"State 3" ... "End state" [fontsize=5, label="CloseChannelTransition", URL="..."];
}
:return: an automata with one sequence of chained states.
:rtype: a :class:`netzob.Model.Grammar.Automata.Automata`
"""
return ChainedStatesAutomataFactory.generate(abstractSession,
symbolList)
def generate(abstractSessions, symbolList):
"""Generate an automata by merging different abstract sessions
in a Prefix Tree Acceptor (PTA).
"""
# Generate chained automatons from the provided list of abstract sessions
from netzob.Inference.Grammar.AutomataFactories.ChainedStatesAutomataFactory import ChainedStatesAutomataFactory
chainedAutomatons = []
for abstractSession in abstractSessions:
chainedAutomaton = ChainedStatesAutomataFactory.generate(
abstractSession, symbolList)
chainedAutomatons.append(chainedAutomaton)
if len(chainedAutomatons) <= 1:
return chainedAutomatons[0]
# Create an initial state/transition for the PTA automaton
ptaInitialState = State("Start state")
idx_state = 0
ptaStateA = State("State " + str(idx_state))
ptaStateA_saved = ptaStateA
ptaTransition = OpenChannelTransition(startState=ptaInitialState,
endState=ptaStateA,
name="Open transition")
# Merge the other automatons in the PTA automaton
for automaton in chainedAutomatons:
# Restore the first main state of the PTA
ptaStateA = ptaStateA_saved
# We go through the first transition (which should be an OpenChannelTransition)
initialState = automaton.initialState
if initialState is not None and len(initialState.transitions) > 0:
transition = initialState.transitions[0]
if isinstance(transition, OpenChannelTransition):
transition = PTAAutomataFactory._getNextChainedTransition(
transition)
if transition is None:
break
# We loop over each state to compare inputSymbol/outputSymbols with the states of the PTA automaton
while True:
# We handle the closing state
if isinstance(transition, CloseChannelTransition):
if len(ptaStateA.transitions) > 0 and isinstance(
ptaStateA.transitions[0],
CloseChannelTransition):
# The transition is equivalent in the PTA
break
else:
# This is a new transition
idx_state += 1
ptaStateB = State("End state " + str(idx_state))
ptaTransition = CloseChannelTransition(
startState=ptaStateA,
endState=ptaStateB,
name="Close transition")
break
inputSymbol = transition.inputSymbol
outputSymbols = transition.outputSymbols
# We do the comparison with the PTA automaton at the transition level
newTransition = True
if len(ptaStateA.transitions) > 0 and isinstance(
ptaStateA.transitions[0], Transition):
if ptaStateA.transitions[0].inputSymbol == inputSymbol:
if len(ptaStateA.transitions[0].outputSymbols
) > 0 and ptaStateA.transitions[
0].outputSymbols[0] == outputSymbols[0]:
# The transition is equivalent in the PTA
newTransition = False
ptaStateA = ptaStateA.transitions[0].endState
if newTransition == True:
idx_state += 1
ptaStateB = State("State " + str(idx_state))
ptaTransition = Transition(
startState=ptaStateA,
endState=ptaStateB,
inputSymbol=inputSymbol,
outputSymbols=[outputSymbols[0]],
name="Transition")
ptaStateA = ptaStateB
transition = PTAAutomataFactory._getNextChainedTransition(
transition)
if transition is None:
break
from netzob.Model.Grammar.Automata import Automata
return Automata(ptaInitialState, symbolList)
def generate(abstractSessions, symbolList):
"""Generate an automata by merging different abstract sessions
in a Prefix Tree Acceptor (PTA).
"""
# Generate chained automatons from the provided list of abstract sessions
from netzob.Inference.Grammar.AutomataFactories.ChainedStatesAutomataFactory import ChainedStatesAutomataFactory
chainedAutomatons = []
for abstractSession in abstractSessions:
chainedAutomaton = ChainedStatesAutomataFactory.generate(abstractSession, symbolList)
chainedAutomatons.append(chainedAutomaton)
if len(chainedAutomatons) <= 1:
return chainedAutomatons[0]
# Create an initial state/transition for the PTA automaton
ptaInitialState = State("Start state")
idx_state = 0
ptaStateA = State("State " + str(idx_state))
ptaStateA_saved = ptaStateA
ptaTransition = OpenChannelTransition(startState=ptaInitialState, endState=ptaStateA, name="Open transition")
# Merge the other automatons in the PTA automaton
for automaton in chainedAutomatons:
# Restore the first main state of the PTA
ptaStateA = ptaStateA_saved
# We go through the first transition (which should be an OpenChannelTransition)
initialState = automaton.initialState
if initialState is not None and len(initialState.transitions) > 0:
transition = initialState.transitions[0]
if isinstance(transition, OpenChannelTransition):
transition = PTAAutomataFactory._getNextChainedTransition(transition)
if transition is None:
break
# We loop over each state to compare inputSymbol/outputSymbols with the states of the PTA automaton
while True:
# We handle the closing state
if isinstance(transition, CloseChannelTransition):
if len(ptaStateA.transitions) > 0 and isinstance(ptaStateA.transitions[0], CloseChannelTransition):
# The transition is equivalent in the PTA
break
else:
# This is a new transition
idx_state += 1
ptaStateB = State("End state " + str(idx_state))
ptaTransition = CloseChannelTransition(startState=ptaStateA, endState=ptaStateB, name="Close transition")
break
inputSymbol = transition.inputSymbol
outputSymbols = transition.outputSymbols
# We do the comparison with the PTA automaton at the transition level
newTransition = True
if len(ptaStateA.transitions) > 0 and isinstance(ptaStateA.transitions[0], Transition):
if ptaStateA.transitions[0].inputSymbol == inputSymbol:
if len(ptaStateA.transitions[0].outputSymbols) > 0 and ptaStateA.transitions[0].outputSymbols[0] == outputSymbols[0]:
# The transition is equivalent in the PTA
newTransition = False
ptaStateA = ptaStateA.transitions[0].endState
if newTransition == True:
idx_state += 1
ptaStateB = State("State " + str(idx_state))
ptaTransition = Transition(startState=ptaStateA, endState=ptaStateB, inputSymbol=inputSymbol, outputSymbols=[outputSymbols[0]], name="Transition")
ptaStateA = ptaStateB
transition = PTAAutomataFactory._getNextChainedTransition(transition)
if transition is None:
break
from netzob.Common.Models.Grammar.Automata import Automata
return Automata(ptaInitialState, symbolList)