def method2format( output, _format="png", mx = None, raw = False ) :
"""
Export method to a specific file format
@param output : output filename
@param _format : format type (png, jpg ...) (default : png)
@param mx : specify the MethodAnalysis object
@param raw : use directly a dot raw buffer
"""
try :
import pydot
except ImportError :
error("module pydot not found")
buff = "digraph code {\n"
buff += "graph [bgcolor=white];\n"
buff += "node [color=lightgray, style=filled shape=box fontname=\"Courier\" fontsize=\"8\"];\n"
if raw == False :
buff += method2dot( mx )
else :
buff += raw
buff += "}"
d = pydot.graph_from_dot_data( buff )
if d :
getattr(d, "write_" + _format)( output )
def method2format(output, _format="png", mx=None, raw=None):
"""
Export method to a specific file format
@param output : output filename
@param _format : format type (png, jpg ...) (default : png)
@param mx : specify the MethodAnalysis object
@param raw : use directly a dot raw buffer if None
"""
try:
import pydot
except ImportError:
error("module pydot not found")
buff = "digraph {\n"
buff += "graph [rankdir=TB]\n"
buff += "node [shape=plaintext]\n"
if raw:
data = raw
else:
data = method2dot(mx)
# subgraphs cluster
buff += "subgraph cluster_" + hashlib.md5(output).hexdigest() + " {\nlabel=\"%s\"\n" % data['name']
buff += data['nodes']
buff += "}\n"
# subgraphs edges
buff += data['edges']
buff += "}\n"
d = pydot.graph_from_dot_data(buff)
if d:
getattr(d, "write_" + _format.lower())(output)
def method2format(
output,
_format='png',
mx=None,
raw=None,
):
"""
Export method to a specific file format
@param output : output filename
@param _format : format type (png, jpg ...) (default : png)
@param mx : specify the MethodAnalysis object
@param raw : use directly a dot raw buffer if None
"""
try:
import pydot
except ImportError:
error('module pydot not found')
buff = 'digraph {\n'
buff += 'graph [rankdir=TB]\n'
buff += 'node [shape=plaintext]\n'
if raw:
data = raw
else:
data = method2dot(mx)
# subgraphs cluster
buff += 'subgraph cluster_' + hashlib.md5(output).hexdigest() \
+ ''' {
label="%s"
''' % data['name']
buff += data['nodes']
buff += '}\n'
# subgraphs edges
buff += data['edges']
buff += '}\n'
d = pydot.graph_from_dot_data(buff)
if d:
getattr(d, 'write_' + _format.lower())(output)
def __init__(self, _analysis) :
self.__analysis = _analysis
try :
from networkx import DiGraph
from networkx import draw_graphviz, write_dot
except ImportError :
error("module networkx not found")
self.__G = DiGraph()
for m in self.__analysis.get_methods() :
for bb in m.basic_blocks.get() :
for trace in bb.stack_traces.get() :
for mre in jvm.MATH_JVM_RE :
if mre[0].match( trace[2].get_name() ) :
for i in trace[3].gets() :
self._add( str(i) )
def __init__(self, _analysis):
self.__analysis = _analysis
try:
from networkx import DiGraph
from networkx import draw_graphviz, write_dot
except ImportError:
error("module networkx not found")
self.__G = DiGraph()
for m in self.__analysis.get_methods():
for bb in m.basic_blocks.get():
for trace in bb.stack_traces.get():
for mre in jvm.MATH_JVM_RE:
if mre[0].match(trace[2].get_name()):
for i in trace[3].gets():
self._add(str(i))
def method2format(output, _format="png", mx=None, raw=None):
"""
Export method to a specific file format
@param output : output filename
@param _format : format type (png, jpg ...) (default : png)
@param mx : specify the MethodAnalysis object
@param raw : use directly a dot raw buffer if None
"""
try:
import pydot
except ImportError:
error("module pydot not found")
buff = "digraph {\n"
buff += "graph [rankdir=TB]\n"
buff += "node [shape=plaintext]\n"
if raw:
data = raw
else:
data = method2dot(mx)
# subgraphs cluster
buff += "subgraph cluster_" + hashlib.md5(
output).hexdigest() + " {\nlabel=\"%s\"\n" % data['name']
buff += data['nodes']
buff += "}\n"
# subgraphs edges
buff += data['edges']
buff += "}\n"
#print buff
d = pydot.graph_from_dot_data(buff)
if d:
getattr(d, "write_" + _format.lower())(output)
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with Androguard. If not, see <http://www.gnu.org/licenses/>.
import hashlib, string, random
from androconf import error
try :
from networkx import DiGraph, all_pairs_dijkstra_path_length, simple_cycles
from networkx import draw_graphviz, write_dot
except ImportError :
error("module networkx not found")
def INIT() :
return WM_L2
# name, access_flags, descriptor
# initial value
# all access (write)
# all dependencies actions (read / write )
class Field :
def __init__(self, _vm, _analysis, _vm_generate, field, offsets, real=False) :
self.__vm = _vm
self.__analysis = _analysis
self.__vm_generate = _vm_generate
self.__field = field
def create_system_method_one_score():
try:
import fuzzy
except ImportError:
error("please install pyfuzzy to use this module !")
import fuzzy.System
import fuzzy.InputVariable
import fuzzy.fuzzify.Plain
import fuzzy.OutputVariable
import fuzzy.defuzzify.COGS
import fuzzy.set.Polygon
import fuzzy.set.Singleton
import fuzzy.set.Triangle
import fuzzy.Adjective
import fuzzy.operator.Input
import fuzzy.operator.Compound
import fuzzy.norm.Min
import fuzzy.norm.Max
import fuzzy.Rule
system = fuzzy.System.System()
input_Length_MS = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_AndroidEntropy_MS = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_JavaEntropy_MS = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Permissions_MS = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
# Input variables
# Length
system.variables["input_Length_MS"] = input_Length_MS
input_Length_MS.adjectives[LOW_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (50.0, 1.0), (100.0, 0.0)])
)
input_Length_MS.adjectives[AVERAGE_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(50.0, 0.0), (100.0, 1.0), (150.0, 1.0), (300.0, 0.0)])
)
input_Length_MS.adjectives[HIGH_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(150.0, 0.0), (200.0, 1.0), (300.0, 1.0), (400.0, 0.0)])
)
input_Length_MS.adjectives[PERFECT_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(350.0, 0.0), (400.0, 1.0), (500.0, 1.0)])
)
# Android Entropy
system.variables["input_AndroidEntropy_MS"] = input_AndroidEntropy_MS
input_AndroidEntropy_MS.adjectives[LOW_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (2.0, 1.0), (4.0, 0.0)])
)
input_AndroidEntropy_MS.adjectives[HIGH_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(3.0, 0.0), (4.0, 1.0), (30.0, 1.0)])
)
# Java Entropy
system.variables["input_JavaEntropy_MS"] = input_JavaEntropy_MS
input_JavaEntropy_MS.adjectives[LOW_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (2.0, 1.0), (4.0, 0.0)])
)
input_JavaEntropy_MS.adjectives[HIGH_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(3.0, 0.0), (4.0, 1.0), (30.0, 1.0)])
)
# Permissions
system.variables["input_Permissions_MS"] = input_Permissions_MS
input_Permissions_MS.adjectives[LOW_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (3.0, 1.0), (4.0, 0.0)])
)
input_Permissions_MS.adjectives[AVERAGE_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(3.0, 0.0), (4.0, 1.0), (8.0, 1.0), (9.0, 0.0)])
)
input_Permissions_MS.adjectives[HIGH_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(8.0, 0.0), (10.0, 1.0), (12.0, 1.0), (13.0, 0.0)])
)
input_Permissions_MS.adjectives[PERFECT_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(12.0, 0.0), (13.0, 1.0), (20.0, 1.0)])
)
# Output variables
output_method_score = fuzzy.OutputVariable.OutputVariable(
defuzzify=fuzzy.defuzzify.COGS.COGS(), description="method one score", min=0.0, max=100.0
)
output_method_score.adjectives[NULL_METHOD_SCORE] = fuzzy.Adjective.Adjective(fuzzy.set.Singleton.Singleton(0.0))
output_method_score.adjectives[AVERAGE_METHOD_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(50.0)
)
output_method_score.adjectives[HIGH_METHOD_SCORE] = fuzzy.Adjective.Adjective(fuzzy.set.Singleton.Singleton(80.0))
output_method_score.adjectives[PERFECT_METHOD_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(100.0)
)
system.variables["output_method_one_score"] = output_method_score
add_system_rule(
system,
"android entropy null",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[NULL_METHOD_SCORE]],
operator=fuzzy.operator.Input.Input(system.variables["input_AndroidEntropy_MS"].adjectives[LOW_SCORE]),
),
)
add_system_rule(
system,
"java entropy null",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[NULL_METHOD_SCORE]],
operator=fuzzy.operator.Input.Input(system.variables["input_JavaEntropy_MS"].adjectives[LOW_SCORE]),
),
)
add_system_rule(
system,
"permissions null",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[NULL_METHOD_SCORE]],
operator=fuzzy.operator.Input.Input(system.variables["input_Permissions_MS"].adjectives[LOW_SCORE]),
),
)
add_system_rule(
system,
"permissions average",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[AVERAGE_METHOD_SCORE]],
operator=fuzzy.operator.Input.Input(system.variables["input_Permissions_MS"].adjectives[AVERAGE_SCORE]),
),
)
add_system_rule(
system,
"permissions high",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[HIGH_METHOD_SCORE]],
operator=fuzzy.operator.Input.Input(system.variables["input_Permissions_MS"].adjectives[HIGH_SCORE]),
),
)
add_system_rule(
system,
"permissions perfect",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[PERFECT_METHOD_SCORE]],
operator=fuzzy.operator.Input.Input(system.variables["input_Permissions_MS"].adjectives[PERFECT_SCORE]),
),
)
add_system_rule(
system,
"length permissions perfect",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[PERFECT_METHOD_SCORE]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Length_MS"].adjectives[PERFECT_SCORE]),
fuzzy.operator.Input.Input(system.variables["input_Permissions_MS"].adjectives[PERFECT_SCORE]),
),
),
)
add_system_rule(
system,
"length AndroidEntropy perfect",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[HIGH_METHOD_SCORE]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Length_MS"].adjectives[PERFECT_SCORE]),
fuzzy.operator.Input.Input(system.variables["input_AndroidEntropy_MS"].adjectives[HIGH_SCORE]),
),
),
)
add_system_rule(
system,
"length JavaEntropy perfect",
fuzzy.Rule.Rule(
adjective=[system.variables["output_method_one_score"].adjectives[HIGH_METHOD_SCORE]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Length_MS"].adjectives[PERFECT_SCORE]),
fuzzy.operator.Input.Input(system.variables["input_JavaEntropy_MS"].adjectives[HIGH_SCORE]),
),
),
)
return system
def create_system_risk():
try:
import fuzzy
except ImportError:
error("please install pyfuzzy to use this module !")
import fuzzy.System
import fuzzy.InputVariable
import fuzzy.fuzzify.Plain
import fuzzy.OutputVariable
import fuzzy.defuzzify.COGS
import fuzzy.set.Polygon
import fuzzy.set.Singleton
import fuzzy.set.Triangle
import fuzzy.Adjective
import fuzzy.operator.Input
import fuzzy.operator.Compound
import fuzzy.norm.Min
import fuzzy.norm.Max
import fuzzy.Rule
system = fuzzy.System.System()
input_Dangerous_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Money_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Privacy_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Binary_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Internet_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
# Input variables
# Dangerous Risk
system.variables["input_Dangerous_Risk"] = input_Dangerous_Risk
input_Dangerous_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (8.0, 1.0), (12.0, 0.0)])
)
input_Dangerous_Risk.adjectives[AVERAGE_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(8.0, 0.0), (16.0, 1.0), (20.0, 0.0)])
)
input_Dangerous_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(16.0, 0.0), (24.0, 1.0)])
)
# Money Risk
system.variables["input_Money_Risk"] = input_Money_Risk
input_Money_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (5.0, 0.0)])
)
input_Money_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(4.0, 0.0), (6.0, 1.0), (30.0, 1.0)])
)
# Privacy Risk
system.variables["input_Privacy_Risk"] = input_Privacy_Risk
input_Privacy_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (6.0, 0.0)])
)
input_Privacy_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(4.0, 0.0), (30.0, 1.0)])
)
# Binary Risk
system.variables["input_Binary_Risk"] = input_Binary_Risk
input_Binary_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (20.0, 0.0)])
)
input_Binary_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(10.0, 0.0), (30.0, 1.0)])
)
# Internet Risk
system.variables["input_Internet_Risk"] = input_Internet_Risk
input_Internet_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (4.0, 0.0)])
)
input_Internet_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(2.0, 0.0), (27.0, 1.0)])
)
# Output variables
output_malware_risk = fuzzy.OutputVariable.OutputVariable(
defuzzify=fuzzy.defuzzify.COGS.COGS(), description="malware risk", min=0.0, max=100.0
)
output_malware_risk.adjectives[NULL_MALWARE_RISK] = fuzzy.Adjective.Adjective(fuzzy.set.Singleton.Singleton(0.0))
output_malware_risk.adjectives[AVERAGE_MALWARE_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(20.0)
)
output_malware_risk.adjectives[HIGH_MALWARE_RISK] = fuzzy.Adjective.Adjective(fuzzy.set.Singleton.Singleton(80.0))
output_malware_risk.adjectives[UNACCEPTABLE_MALWARE_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(100.0)
)
system.variables["output_malware_risk"] = output_malware_risk
# Rules
# RULE 1 : IF input_Dangerous_Risk IS Low THEN output_risk_malware IS Null;
add_system_rule(
system,
"r1",
fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[NULL_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input(system.variables["input_Dangerous_Risk"].adjectives[LOW_RISK]),
),
)
# RULE 2 : IF input_Dangerous_Risk IS Average THEN output_risk_malware IS Average;
add_system_rule(
system,
"r2",
fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[AVERAGE_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input(system.variables["input_Dangerous_Risk"].adjectives[AVERAGE_RISK]),
),
)
# RULE 3 : IF input_Dangerous_Risk IS High THEN output_risk_malware IS High;
add_system_rule(
system,
"r3",
fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[HIGH_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input(system.variables["input_Dangerous_Risk"].adjectives[HIGH_RISK]),
),
)
# RULE 4 : IF input_Dangerous_Risk IS Low AND input_Binary_Risk IS High THEN output_risk_malware IS High;
add_system_rule(
system,
"r4",
fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[HIGH_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input(system.variables["input_Binary_Risk"].adjectives[HIGH_RISK]),
),
)
# RULE 5 : IF input_Money_Risk IS High THEN output_risk_malware IS Unacceptable;
add_system_rule(
system,
"r5",
fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[UNACCEPTABLE_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input(system.variables["input_Money_Risk"].adjectives[HIGH_RISK]),
),
)
# RULE 6 : IF input_Dangerous_Risk IS High AND input_Binary_Risk IS High THEN output_risk_malware IS Unacceptable;
add_system_rule(
system,
"r6",
fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[UNACCEPTABLE_MALWARE_RISK]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Dangerous_Risk"].adjectives[HIGH_RISK]),
fuzzy.operator.Input.Input(system.variables["input_Binary_Risk"].adjectives[HIGH_RISK]),
),
),
)
# RULE 7 : IF input_Internet_Risk IS Low AND input_Privacy_Risk IS High THEN output_risk_malware IS High;
add_system_rule(
system,
"r7",
fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[HIGH_MALWARE_RISK]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Internet_Risk"].adjectives[LOW_RISK]),
fuzzy.operator.Input.Input(system.variables["input_Privacy_Risk"].adjectives[HIGH_RISK]),
),
),
)
# RULE 8 : IF input_Internet_Risk IS High AND input_Privacy_Risk IS High THEN output_risk_malware IS Unacceptable;
add_system_rule(
system,
"r8",
fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[UNACCEPTABLE_MALWARE_RISK]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Internet_Risk"].adjectives[HIGH_RISK]),
fuzzy.operator.Input.Input(system.variables["input_Privacy_Risk"].adjectives[HIGH_RISK]),
),
),
)
return system
def create_system() :
try :
import fuzzy
except ImportError :
error("please install pyfuzzy to use this module !")
import fuzzy.System
import fuzzy.InputVariable
import fuzzy.fuzzify.Plain
import fuzzy.OutputVariable
import fuzzy.defuzzify.COGS
import fuzzy.set.Polygon
import fuzzy.set.Singleton
import fuzzy.set.Triangle
import fuzzy.Adjective
import fuzzy.operator.Input
import fuzzy.operator.Compound
import fuzzy.norm.Min
import fuzzy.norm.Max
import fuzzy.Rule
system = fuzzy.System.System()
input_Dangerous_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Money_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Privacy_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Binary_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Internet_Risk = fuzzy.InputVariable.InputVariable(fuzzify=fuzzy.fuzzify.Plain.Plain())
# Input variables
# Dangerous Risk
system.variables["input_Dangerous_Risk"] = input_Dangerous_Risk
in1_set = fuzzy.set.Polygon.Polygon([(0.0, 1.0), (8.0, 1.0), (12.0, 0.0)])
in1 = fuzzy.Adjective.Adjective(in1_set)
input_Dangerous_Risk.adjectives[LOW_RISK] = in1
in2_set = fuzzy.set.Polygon.Polygon([(8.0, 0.0), (16.0, 1.0), (20.0, 0.0)])
in2 = fuzzy.Adjective.Adjective(in2_set)
input_Dangerous_Risk.adjectives[AVERAGE_RISK] = in2
in3_set = fuzzy.set.Polygon.Polygon([(16.0, 0.0), (24.0, 1.0)])
in3 = fuzzy.Adjective.Adjective(in3_set)
input_Dangerous_Risk.adjectives[HIGH_RISK] = in3
# Money Risk
system.variables["input_Money_Risk"] = input_Money_Risk
in4_set = fuzzy.set.Polygon.Polygon([(0.0, 1.0), (5.0, 0.0)])
in4 = fuzzy.Adjective.Adjective(in4_set)
input_Money_Risk.adjectives[LOW_RISK] = in4
in5_set = fuzzy.set.Polygon.Polygon([(4.0, 0.0), (6.0, 1.0), (30.0, 1.0)])
in5 = fuzzy.Adjective.Adjective(in5_set)
input_Money_Risk.adjectives[HIGH_RISK] = in5
# Privacy Risk
system.variables["input_Privacy_Risk"] = input_Privacy_Risk
in6_set = fuzzy.set.Polygon.Polygon([(0.0, 1.0), (6.0, 0.0)])
in6 = fuzzy.Adjective.Adjective(in6_set)
input_Privacy_Risk.adjectives[LOW_RISK] = in6
in7_set = fuzzy.set.Polygon.Polygon([(4.0, 0.0), (30.0, 1.0)])
in7 = fuzzy.Adjective.Adjective(in7_set)
input_Privacy_Risk.adjectives[HIGH_RISK] = in7
# Binary Risk
system.variables["input_Binary_Risk"] = input_Binary_Risk
in8_set = fuzzy.set.Polygon.Polygon([(0.0, 1.0), (20.0, 0.0)])
in8 = fuzzy.Adjective.Adjective(in8_set)
input_Binary_Risk.adjectives[LOW_RISK] = in8
in9_set = fuzzy.set.Polygon.Polygon([(10.0, 0.0), (30.0, 1.0)])
in9 = fuzzy.Adjective.Adjective(in9_set)
input_Binary_Risk.adjectives[HIGH_RISK] = in9
# Internet Risk
system.variables["input_Internet_Risk"] = input_Internet_Risk
in10_set = fuzzy.set.Polygon.Polygon([(0.0, 1.0), (4.0, 0.0)])
in10 = fuzzy.Adjective.Adjective(in10_set)
input_Internet_Risk.adjectives[LOW_RISK] = in10
in11_set = fuzzy.set.Polygon.Polygon([(2.0, 0.0), (27.0, 1.0)])
in11 = fuzzy.Adjective.Adjective(in11_set)
input_Internet_Risk.adjectives[HIGH_RISK] = in11
# Output variables
output_malware_risk = fuzzy.OutputVariable.OutputVariable(
defuzzify=fuzzy.defuzzify.COGS.COGS(),
description="malware risk",
min=0.0,max=100.0,
)
output_malware_risk.adjectives[NULL_MALWARE_RISK] = fuzzy.Adjective.Adjective(fuzzy.set.Singleton.Singleton(0.0))
output_malware_risk.adjectives[AVERAGE_MALWARE_RISK] = fuzzy.Adjective.Adjective(fuzzy.set.Singleton.Singleton(20.0))
output_malware_risk.adjectives[HIGH_MALWARE_RISK] = fuzzy.Adjective.Adjective(fuzzy.set.Singleton.Singleton(80.0))
output_malware_risk.adjectives[UNACCEPTABLE_MALWARE_RISK] = fuzzy.Adjective.Adjective(fuzzy.set.Singleton.Singleton(100.0))
system.variables["output_malware_risk"] = output_malware_risk
# Rules
#RULE 1 : IF input_Dangerous_Risk IS Faible THEN output_risk_malware IS Nul;
rule1 = fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[NULL_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input( system.variables["input_Dangerous_Risk"].adjectives[LOW_RISK] )
)
#RULE 2 : IF input_Dangerous_Risk IS Moyen THEN output_risk_malware IS Moyen;
rule2 = fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[AVERAGE_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input( system.variables["input_Dangerous_Risk"].adjectives[AVERAGE_RISK] )
)
#RULE 3 : IF input_Dangerous_Risk IS Eleve THEN output_risk_malware IS Eleve;
rule3 = fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[HIGH_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input( system.variables["input_Dangerous_Risk"].adjectives[HIGH_RISK] )
)
#RULE 4 : IF input_Dangerous_Risk IS Faible AND input_Binary_Risk IS Eleve THEN output_risk_malware IS Eleve;
rule4 = fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[HIGH_MALWARE_RISK]],
operator = fuzzy.operator.Input.Input( system.variables["input_Binary_Risk"].adjectives[HIGH_RISK] )
)
#RULE 5 : IF input_Money_Risk IS Eleve THEN output_risk_malware IS Inacceptable;
rule5 = fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[UNACCEPTABLE_MALWARE_RISK]],
operator=fuzzy.operator.Input.Input( system.variables["input_Money_Risk"].adjectives[HIGH_RISK] )
)
#RULE 6 : IF input_Dangerous_Risk IS Eleve AND input_Binary_Risk IS Eleve THEN output_risk_malware IS Inacceptable;
rule6 = fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[UNACCEPTABLE_MALWARE_RISK]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input( system.variables["input_Dangerous_Risk"].adjectives[HIGH_RISK] ),
fuzzy.operator.Input.Input( system.variables["input_Binary_Risk"].adjectives[HIGH_RISK] ) )
)
#RULE 7 : IF input_Internet_Risk IS Faible AND input_Privacy_Risk IS Eleve THEN output_risk_malware IS Eleve;
rule7 = fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[HIGH_MALWARE_RISK]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input( system.variables["input_Internet_Risk"].adjectives[LOW_RISK] ),
fuzzy.operator.Input.Input( system.variables["input_Privacy_Risk"].adjectives[HIGH_RISK] ) )
)
#RULE 8 : IF input_Internet_Risk IS Eleve AND input_Privacy_Risk IS Eleve THEN output_risk_malware IS Inacceptable;
rule8 = fuzzy.Rule.Rule(
adjective=[system.variables["output_malware_risk"].adjectives[UNACCEPTABLE_MALWARE_RISK]],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input( system.variables["input_Internet_Risk"].adjectives[HIGH_RISK] ),
fuzzy.operator.Input.Input( system.variables["input_Privacy_Risk"].adjectives[HIGH_RISK] ) )
)
system.rules["r1"] = rule1
system.rules["r2"] = rule2
system.rules["r3"] = rule3
system.rules["r4"] = rule4
system.rules["r5"] = rule5
system.rules["r6"] = rule6
system.rules["r7"] = rule7
system.rules["r8"] = rule8
return system
+ r['subgraphs'].append({ 'name': method.m.class_name+"."+method.m.name+"->"+method.m.get_descriptor(), 'nodes' : blocks_html, 'edges' : edges_html })
+
+ return r
+
+def diff2format( output, _format="png", data = False ) :
+ """
+ Export method to a specific file format
+
+ @param output : output filename
+ @param _format : format type (png, jpg ...) (default : png)
+ @param raw : subgraphs dicts list
+ """
+ try :
+ import pydot
+ except ImportError :
+ error("module pydot not found")
+
+ buff = "digraph {\n"
+ buff += "graph [ rankdir=TB]\n"
+ buff += "node [shape=plaintext]\n"
+
+ i=0
+ # subgraphs cluster
+ for subgraph in data['subgraphs']:
+ buff += "subgraph cluster_" + hashlib.md5(output).hexdigest() + str(i) + " {\nlabel=\"%s\"\n" % subgraph['name']
+ buff += subgraph['nodes']
+ buff += "}\n"
+ i+=1
+
+ # subgraphs edges
+ buff += subgraph['edges']
def create_system_method_one_score():
try:
import fuzzy
except ImportError:
error("please install pyfuzzy to use this module !")
import fuzzy.System
import fuzzy.InputVariable
import fuzzy.fuzzify.Plain
import fuzzy.OutputVariable
import fuzzy.defuzzify.COGS
import fuzzy.set.Polygon
import fuzzy.set.Singleton
import fuzzy.set.Triangle
import fuzzy.Adjective
import fuzzy.operator.Input
import fuzzy.operator.Compound
import fuzzy.norm.Min
import fuzzy.norm.Max
import fuzzy.Rule
system = fuzzy.System.System()
input_Length_MS = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
input_AndroidEntropy_MS = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
input_JavaEntropy_MS = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Permissions_MS = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
# Input variables
# Length
system.variables["input_Length_MS"] = input_Length_MS
input_Length_MS.adjectives[LOW_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (50.0, 1.0), (100.0, 0.0)]))
input_Length_MS.adjectives[AVERAGE_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(50.0, 0.0), (100.0, 1.0), (150.0, 1.0),
(300.0, 0.0)]))
input_Length_MS.adjectives[HIGH_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(150.0, 0.0), (200.0, 1.0), (300.0, 1.0),
(400.0, 0.0)]))
input_Length_MS.adjectives[PERFECT_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(350.0, 0.0), (400.0, 1.0), (500.0, 1.0)]))
# Android Entropy
system.variables["input_AndroidEntropy_MS"] = input_AndroidEntropy_MS
input_AndroidEntropy_MS.adjectives[LOW_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (2.0, 1.0), (4.0, 0.0)]))
input_AndroidEntropy_MS.adjectives[HIGH_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(3.0, 0.0), (4.0, 1.0), (30.0, 1.0)]))
# Java Entropy
system.variables["input_JavaEntropy_MS"] = input_JavaEntropy_MS
input_JavaEntropy_MS.adjectives[LOW_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (2.0, 1.0), (4.0, 0.0)]))
input_JavaEntropy_MS.adjectives[HIGH_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(3.0, 0.0), (4.0, 1.0), (30.0, 1.0)]))
# Permissions
system.variables["input_Permissions_MS"] = input_Permissions_MS
input_Permissions_MS.adjectives[LOW_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (3.0, 1.0), (4.0, 0.0)]))
input_Permissions_MS.adjectives[AVERAGE_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(3.0, 0.0), (4.0, 1.0), (8.0, 1.0),
(9.0, 0.0)]))
input_Permissions_MS.adjectives[HIGH_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(8.0, 0.0), (10.0, 1.0), (12.0, 1.0),
(13.0, 0.0)]))
input_Permissions_MS.adjectives[PERFECT_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(12.0, 0.0), (13.0, 1.0), (20.0, 1.0)]))
# Output variables
output_method_score = fuzzy.OutputVariable.OutputVariable(
defuzzify=fuzzy.defuzzify.COGS.COGS(),
description="method one score",
min=0.0,
max=100.0,
)
output_method_score.adjectives[
NULL_METHOD_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(0.0))
output_method_score.adjectives[
AVERAGE_METHOD_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(50.0))
output_method_score.adjectives[
HIGH_METHOD_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(80.0))
output_method_score.adjectives[
PERFECT_METHOD_SCORE] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(100.0))
system.variables["output_method_one_score"] = output_method_score
add_system_rule(
system, "android entropy null",
fuzzy.Rule.Rule(adjective=[
system.variables["output_method_one_score"].
adjectives[NULL_METHOD_SCORE]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_AndroidEntropy_MS"].
adjectives[LOW_SCORE])))
add_system_rule(
system, "java entropy null",
fuzzy.Rule.Rule(adjective=[
system.variables["output_method_one_score"].
adjectives[NULL_METHOD_SCORE]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_JavaEntropy_MS"].
adjectives[LOW_SCORE])))
add_system_rule(
system, "permissions null",
fuzzy.Rule.Rule(adjective=[
system.variables["output_method_one_score"].
adjectives[NULL_METHOD_SCORE]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Permissions_MS"].
adjectives[LOW_SCORE])))
add_system_rule(
system, "permissions average",
fuzzy.Rule.Rule(adjective=[
system.variables["output_method_one_score"].
adjectives[AVERAGE_METHOD_SCORE]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Permissions_MS"].
adjectives[AVERAGE_SCORE])))
add_system_rule(
system, "permissions high",
fuzzy.Rule.Rule(adjective=[
system.variables["output_method_one_score"].
adjectives[HIGH_METHOD_SCORE]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Permissions_MS"].
adjectives[HIGH_SCORE])))
add_system_rule(
system, "permissions perfect",
fuzzy.Rule.Rule(adjective=[
system.variables["output_method_one_score"].
adjectives[PERFECT_METHOD_SCORE]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Permissions_MS"].
adjectives[PERFECT_SCORE])))
add_system_rule(
system, "length permissions perfect",
fuzzy.Rule.Rule(
adjective=[
system.variables["output_method_one_score"].
adjectives[PERFECT_METHOD_SCORE]
],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Length_MS"].
adjectives[PERFECT_SCORE]),
fuzzy.operator.Input.Input(
system.variables["input_Permissions_MS"].
adjectives[PERFECT_SCORE]))))
add_system_rule(
system, "length AndroidEntropy perfect",
fuzzy.Rule.Rule(
adjective=[
system.variables["output_method_one_score"].
adjectives[HIGH_METHOD_SCORE]
],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Length_MS"].
adjectives[PERFECT_SCORE]),
fuzzy.operator.Input.Input(
system.variables["input_AndroidEntropy_MS"].
adjectives[HIGH_SCORE]))))
add_system_rule(
system, "length JavaEntropy perfect",
fuzzy.Rule.Rule(
adjective=[
system.variables["output_method_one_score"].
adjectives[HIGH_METHOD_SCORE]
],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(system.variables["input_Length_MS"].
adjectives[PERFECT_SCORE]),
fuzzy.operator.Input.Input(
system.variables["input_JavaEntropy_MS"].
adjectives[HIGH_SCORE]))))
return system
def create_system_risk():
try:
import fuzzy
except ImportError:
error("please install pyfuzzy to use this module !")
import fuzzy.System
import fuzzy.InputVariable
import fuzzy.fuzzify.Plain
import fuzzy.OutputVariable
import fuzzy.defuzzify.COGS
import fuzzy.set.Polygon
import fuzzy.set.Singleton
import fuzzy.set.Triangle
import fuzzy.Adjective
import fuzzy.operator.Input
import fuzzy.operator.Compound
import fuzzy.norm.Min
import fuzzy.norm.Max
import fuzzy.Rule
system = fuzzy.System.System()
input_Dangerous_Risk = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Money_Risk = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Privacy_Risk = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Binary_Risk = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
input_Internet_Risk = fuzzy.InputVariable.InputVariable(
fuzzify=fuzzy.fuzzify.Plain.Plain())
# Input variables
# Dangerous Risk
system.variables["input_Dangerous_Risk"] = input_Dangerous_Risk
input_Dangerous_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (8.0, 1.0), (12.0, 0.0)]))
input_Dangerous_Risk.adjectives[AVERAGE_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(8.0, 0.0), (16.0, 1.0), (20.0, 0.0)]))
input_Dangerous_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(16.0, 0.0), (24.0, 1.0)]))
# Money Risk
system.variables["input_Money_Risk"] = input_Money_Risk
input_Money_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (5.0, 0.0)]))
input_Money_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(4.0, 0.0), (6.0, 1.0), (30.0, 1.0)]))
# Privacy Risk
system.variables["input_Privacy_Risk"] = input_Privacy_Risk
input_Privacy_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (6.0, 0.0)]))
input_Privacy_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(4.0, 0.0), (30.0, 1.0)]))
# Binary Risk
system.variables["input_Binary_Risk"] = input_Binary_Risk
input_Binary_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (20.0, 0.0)]))
input_Binary_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(10.0, 0.0), (30.0, 1.0)]))
# Internet Risk
system.variables["input_Internet_Risk"] = input_Internet_Risk
input_Internet_Risk.adjectives[LOW_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(0.0, 1.0), (4.0, 0.0)]))
input_Internet_Risk.adjectives[HIGH_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Polygon.Polygon([(2.0, 0.0), (27.0, 1.0)]))
# Output variables
output_malware_risk = fuzzy.OutputVariable.OutputVariable(
defuzzify=fuzzy.defuzzify.COGS.COGS(),
description="malware risk",
min=0.0,
max=100.0,
)
output_malware_risk.adjectives[
NULL_MALWARE_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(0.0))
output_malware_risk.adjectives[
AVERAGE_MALWARE_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(20.0))
output_malware_risk.adjectives[
HIGH_MALWARE_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(80.0))
output_malware_risk.adjectives[
UNACCEPTABLE_MALWARE_RISK] = fuzzy.Adjective.Adjective(
fuzzy.set.Singleton.Singleton(100.0))
system.variables["output_malware_risk"] = output_malware_risk
# Rules
#RULE 1 : IF input_Dangerous_Risk IS Low THEN output_risk_malware IS Null;
add_system_rule(
system, "r1",
fuzzy.Rule.Rule(adjective=[
system.variables["output_malware_risk"].
adjectives[NULL_MALWARE_RISK]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Dangerous_Risk"].
adjectives[LOW_RISK])))
#RULE 2 : IF input_Dangerous_Risk IS Average THEN output_risk_malware IS Average;
add_system_rule(
system, "r2",
fuzzy.Rule.Rule(adjective=[
system.variables["output_malware_risk"].
adjectives[AVERAGE_MALWARE_RISK]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Dangerous_Risk"].
adjectives[AVERAGE_RISK])))
#RULE 3 : IF input_Dangerous_Risk IS High THEN output_risk_malware IS High;
add_system_rule(
system, "r3",
fuzzy.Rule.Rule(adjective=[
system.variables["output_malware_risk"].
adjectives[HIGH_MALWARE_RISK]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Dangerous_Risk"].
adjectives[HIGH_RISK])))
#RULE 4 : IF input_Dangerous_Risk IS Low AND input_Binary_Risk IS High THEN output_risk_malware IS High;
add_system_rule(
system, "r4",
fuzzy.Rule.Rule(
adjective=[
system.variables["output_malware_risk"].
adjectives[HIGH_MALWARE_RISK]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Binary_Risk"].adjectives[HIGH_RISK])))
#RULE 5 : IF input_Money_Risk IS High THEN output_risk_malware IS Unacceptable;
add_system_rule(
system, "r5",
fuzzy.Rule.Rule(
adjective=[
system.variables["output_malware_risk"].
adjectives[UNACCEPTABLE_MALWARE_RISK]
],
operator=fuzzy.operator.Input.Input(
system.variables["input_Money_Risk"].adjectives[HIGH_RISK])))
#RULE 6 : IF input_Dangerous_Risk IS High AND input_Binary_Risk IS High THEN output_risk_malware IS Unacceptable;
add_system_rule(
system, "r6",
fuzzy.Rule.Rule(adjective=[
system.variables["output_malware_risk"].
adjectives[UNACCEPTABLE_MALWARE_RISK]
],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(
system.variables["input_Dangerous_Risk"].
adjectives[HIGH_RISK]),
fuzzy.operator.Input.Input(
system.variables["input_Binary_Risk"].
adjectives[HIGH_RISK]))))
#RULE 7 : IF input_Internet_Risk IS Low AND input_Privacy_Risk IS High THEN output_risk_malware IS High;
add_system_rule(
system, "r7",
fuzzy.Rule.Rule(adjective=[
system.variables["output_malware_risk"].
adjectives[HIGH_MALWARE_RISK]
],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(
system.variables["input_Internet_Risk"].
adjectives[LOW_RISK]),
fuzzy.operator.Input.Input(
system.variables["input_Privacy_Risk"].
adjectives[HIGH_RISK]))))
#RULE 8 : IF input_Internet_Risk IS High AND input_Privacy_Risk IS High THEN output_risk_malware IS Unacceptable;
add_system_rule(
system, "r8",
fuzzy.Rule.Rule(adjective=[
system.variables["output_malware_risk"].
adjectives[UNACCEPTABLE_MALWARE_RISK]
],
operator=fuzzy.operator.Compound.Compound(
fuzzy.norm.Min.Min(),
fuzzy.operator.Input.Input(
system.variables["input_Internet_Risk"].
adjectives[HIGH_RISK]),
fuzzy.operator.Input.Input(
system.variables["input_Privacy_Risk"].
adjectives[HIGH_RISK]))))
return system