def range2bdd(min_value, max_value, index, interval=(0, 2**8 - 1)):
"""Static method for construction ROBDD range
Parameters
----------
min_value : int. the start of the range
max_value : the end of the range
index : the index for ROBDD construction variable name
interval : (int, int). the interval to check (used for recursivity)
Return
------
Return the corresponding ROBDD
"""
if interval[0] > max_value or interval[1] < min_value:
return Robdd.false()
if interval[0] >= min_value and interval[1] <= max_value:
return Protocol(interval[0]).toBDD(index, int(math.log(interval[1] + 1 - interval[0], 2)))
def new_min_interval(inter):
min_v = inter[0]
max_v = inter[0] + 2**(int(math.log(inter[1] + 1 - inter[0], 2)) - 1) - 1
return (min_v, max_v)
def new_max_interval(inter):
min_v = inter[0] + 2**(int(math.log(inter[1] + 1 - inter[0], 2)) - 1)
max_v = inter[1]
return (min_v, max_v)
return synthesize(Protocol.range2bdd(min_value, max_value, index, new_min_interval(interval)),
Bdd.OR,
Protocol.range2bdd(min_value, max_value, index, new_max_interval(interval)))
def _tree_parse_detection(self, tree_path):
"""Detect anomalies given a rooted tree
Parameters
----------
tree_path : nested list. Rooted tree representation
Return
------
Return a tuple containing the accepted paquet and a list of detected errors
"""
error_list = []
parent = tree_path[0]
remain = Robdd.false()
for i in xrange(1, len(tree_path)):
acl_list = NetworkGraph.NetworkGraph().get_acl_list(
src=tree_path[i][0], dst=parent)
# test is leaf
if len(tree_path[i]) == 1:
res_remain, res_error = self._distributed_detection(
acl_list, Robdd.true(), tree_path[i])
res_error = []
else:
res_remain, res_error = self._tree_parse_detection(
tree_path[i])
error_list += res_error
res_remain, res_error = self._distributed_detection(
acl_list, res_remain, tree_path[i])
error_list += res_error
remain = synthesize(remain, Bdd.OR, res_remain)
return remain, error_list
def _tree_parse_detection(self, tree_path):
"""Detect anomalies given a rooted tree
Parameters
----------
tree_path : nested list. Rooted tree representation
Return
------
Return a tuple containing the accepted packets and a list of detected errors
"""
error_list = []
parent = tree_path[0]
remain = Robdd.false()
for i in xrange(1, len(tree_path)):
acl_list = NetworkGraph.NetworkGraph().get_acl_list(src=tree_path[i][0], dst=parent)
# test is leaf
if len(tree_path[i]) == 1:
res_remain, res_error = self._distributed_detection(acl_list, Robdd.true(), tree_path[i])
res_error = []
else:
res_remain, res_error = self._tree_parse_detection(tree_path[i])
error_list += res_error
res_remain, res_error = self._distributed_detection(acl_list, res_remain, tree_path[i])
error_list += res_error
remain = synthesize(remain, Bdd.OR, res_remain)
return remain, error_list
def range2bdd(min_value, max_value, index, interval=(0, 2**8 - 1)):
"""Static method for construction ROBDD range
Parameters
----------
min_value : int. the start of the range
max_value : the end of the range
index : the index for ROBDD construction variable name
interval : (int, int). the interval to check (used for recursivity)
Return
------
Return the corresponding ROBDD
"""
if interval[0] > max_value or interval[1] < min_value:
return Robdd.false()
if interval[0] >= min_value and interval[1] <= max_value:
return Protocol(interval[0]).toBDD(index, int(math.log(interval[1] + 1 - interval[0], 2)))
def new_min_interval(inter):
min_v = inter[0]
max_v = inter[0] + 2**(int(math.log(inter[1] + 1 - inter[0], 2)) - 1) - 1
return (min_v, max_v)
def new_max_interval(inter):
min_v = inter[0] + 2**(int(math.log(inter[1] + 1 - inter[0], 2)) - 1)
max_v = inter[1]
return (min_v, max_v)
return synthesize(Protocol.range2bdd(min_value, max_value, index, new_min_interval(interval)),
Bdd.OR,
Protocol.range2bdd(min_value, max_value, index, new_max_interval(interval)))
def toBDD(self):
"""Compute rhe ROBDD of the rule if rule_robdd is None else return rule_robdd
Return
------
Return the ROBDD
"""
if self.rule_robdd is None:
rule_robdd = Robdd.true()
protocol_bdd = Robdd.false()
ip_src_bdd = Robdd.false()
port_src_bdd = Robdd.false()
ip_dst_bdd = Robdd.false()
port_dst_bdd = Robdd.false()
for i in self.protocol:
protocol_bdd = synthesize(protocol_bdd, Bdd.OR, i.toBDD(0))
if self.protocol:
rule_robdd = synthesize(rule_robdd, Bdd.AND, protocol_bdd)
for i in self.ip_source:
ip_src_bdd = synthesize(ip_src_bdd, Bdd.OR, i.toBDD(8))
if self.ip_source:
rule_robdd = synthesize(rule_robdd, Bdd.AND, ip_src_bdd)
for i in self.port_source:
port_src_bdd = synthesize(port_src_bdd, Bdd.OR, i.toBDD(40))
if self.port_source:
rule_robdd = synthesize(rule_robdd, Bdd.AND, port_src_bdd)
for i in self.ip_dest:
ip_dst_bdd = synthesize(ip_dst_bdd, Bdd.OR, i.toBDD(56))
if self.ip_dest:
rule_robdd = synthesize(rule_robdd, Bdd.AND, ip_dst_bdd)
for i in self.port_dest:
port_dst_bdd = synthesize(port_dst_bdd, Bdd.OR, i.toBDD(88))
if self.port_dest:
rule_robdd = synthesize(rule_robdd, Bdd.AND, port_dst_bdd)
self.rule_robdd = rule_robdd
return self.rule_robdd
def toBDD(self):
"""Compute rhe ROBDD of the rule if rule_robdd is None else return rule_robdd
Return
------
Return the ROBDD
"""
if self.rule_robdd is None:
rule_robdd = Robdd.true()
protocol_bdd = Robdd.false()
ip_src_bdd = Robdd.false()
port_src_bdd = Robdd.false()
ip_dst_bdd = Robdd.false()
port_dst_bdd = Robdd.false()
for i in self.protocol:
protocol_bdd = synthesize(protocol_bdd, Bdd.OR, i.toBDD(0))
if self.protocol:
rule_robdd = synthesize(rule_robdd, Bdd.AND, protocol_bdd)
for i in self.ip_source:
ip_src_bdd = synthesize(ip_src_bdd, Bdd.OR, i.toBDD(8))
if self.ip_source:
rule_robdd = synthesize(rule_robdd, Bdd.AND, ip_src_bdd)
for i in self.port_source:
port_src_bdd = synthesize(port_src_bdd, Bdd.OR, i.toBDD(40))
if self.port_source:
rule_robdd = synthesize(rule_robdd, Bdd.AND, port_src_bdd)
for i in self.ip_dest:
ip_dst_bdd = synthesize(ip_dst_bdd, Bdd.OR, i.toBDD(56))
if self.ip_dest:
rule_robdd = synthesize(rule_robdd, Bdd.AND, ip_dst_bdd)
for i in self.port_dest:
port_dst_bdd = synthesize(port_dst_bdd, Bdd.OR, i.toBDD(88))
if self.port_dest:
rule_robdd = synthesize(rule_robdd, Bdd.AND, port_dst_bdd)
self.rule_robdd = rule_robdd
return self.rule_robdd
def toBDD(self, index):
"""Compute the corresponding ROBDD of the Ip
Parameters
----------
index : int. The start point of the variable name
Return
------
Return the corresponding ROBDD
"""
# Ip: 32 bits
res = Robdd.true()
ip_size = 32
for i in range(ip_size - (32 - Ip.MaskToCidr(self.mask))):
if (self.ip >> (ip_size - i - 1)) & 1:
res = synthesize(res, Bdd.AND, Robdd.make_x(index + i))
else:
res = synthesize(res, Bdd.AND, Robdd.make_not_x(index + i))
return res
def toBDD(self, index):
"""Compute the corresponding ROBDD of the Ip
Parameters
----------
index : int. The start point of the variable name
Return
------
Return the corresponding ROBDD
"""
# Ip: 32 bits
res = Robdd.true()
ip_size = 32
for i in range(ip_size - (32 - Ip.MaskToCidr(self.mask))):
if (self.ip >> (ip_size - i - 1)) & 1:
res = synthesize(res, Bdd.AND, Robdd.make_x(index + i))
else:
res = synthesize(res, Bdd.AND, Robdd.make_not_x(index + i))
return res
def toBDD(self, index, limit=0):
"""Compute the ROBDD.
Parameters
----------
index : int. Used for ROBDD variable index
limit : int (optional, default=0). The limit bit used for range representation.
Return
------
Return the comuted ROBDD
"""
# Port: 16 bits
res = Robdd.true()
port_size = 16
for i in range(port_size - limit):
if (self.port >> (port_size - i - 1)) & 1:
res = synthesize(res, Bdd.AND, Robdd.make_x(index + i))
else:
res = synthesize(res, Bdd.AND, Robdd.make_not_x(index + i))
return res
def toBDD(self, index, limit=0):
"""Construct the ROBDD.
Parameters
----------
index : int. Used for ROBDD variable index
limit : int (optional, default=0). The limit bit used for range representation
Return
------
Return the computed ROBDD.
"""
# Protocol : 8 bits
res = Robdd.true()
protocol_size = 8
for i in range(protocol_size - limit):
if (self.protocol >> (protocol_size - i - 1)) & 1:
res = synthesize(res, Bdd.AND, Robdd.make_x(index + i))
else:
res = synthesize(res, Bdd.AND, Robdd.make_not_x(index + i))
return res
def toBDD(self, index, limit=0):
"""Compute the ROBDD.
Parameters
----------
index : int. Used for ROBDD variable index
limit : int (optional, default=0). The limit bit used for range representation.
Return
------
Return the comuted ROBDD
"""
# Port: 16 bits
res = Robdd.true()
port_size = 16
for i in range(port_size - limit):
if (self.port >> (port_size - i - 1)) & 1:
res = synthesize(res, Bdd.AND, Robdd.make_x(index + i))
else:
res = synthesize(res, Bdd.AND, Robdd.make_not_x(index + i))
return res
def toBDD(self, index, limit=0):
"""Construct the ROBDD.
Parameters
----------
index : int. Used for ROBDD variable index
limit : int (optional, default=0). The limit bit used for range representation
Return
------
Return the computed ROBDD.
"""
# Protocol : 8 bits
res = Robdd.true()
protocol_size = 8
for i in range(protocol_size - limit):
if (self.protocol >> (protocol_size - i - 1)) & 1:
res = synthesize(res, Bdd.AND, Robdd.make_x(index + i))
else:
res = synthesize(res, Bdd.AND, Robdd.make_not_x(index + i))
return res
def _detect_anomaly(acl, stack, parsed_rule, visited, remain, accept, deny, result_queue, processed_id_rules, deep_search):
"""Recursive algorithm for ACL graph traversal.
This recursive algorithm construct the list of all possible list of rule for the ACL chained model.
Then it launch the _classify_anomaly function on each rule and try to detect anomaly if any.
Parameters
----------
acl : ACL. The acl to inspect.
stack : list of Rule list. A stack of rule list.
parsed_rule : Rule list. A list of parse rule.
visisted : ACL list. List of visited ACL.
remain : ROBDD. Remain ROBDD.
accept : ROBDD. Accept ROBDD.
deny : ROBDD. Deny ROBDD.
result_queue : list. List of error.
processed_id_rules : list. List of processed rules.
deep_search : Bool. If true find blamed rules.
"""
if acl not in visited:
visited.append(acl)
stack.append(list(acl.rules))
while stack:
rule_list = stack[-1]
while rule_list:
rule = rule_list.pop(0)
processed_id_rules.put(rule.identifier)
_classify_anomaly(rule, parsed_rule, remain, accept, deny, result_queue, deep_search)
parsed_rule.append([rule, rule.action.chain, rule.toBDD()])
if rule.action.is_chained() or rule.action.is_return():
# a = accept & True -> copy of accept
a = synthesize(accept, Bdd.AND, Robdd.true())
# d = D ∪ (R ∩ ¬P)
d = synthesize(deny, Bdd.OR, synthesize(remain, Bdd.AND, negate_bdd(rule.toBDD())))
# r = I ∩ ¬(a ∪ d)
r = synthesize(Robdd.true(), Bdd.AND, negate_bdd(synthesize(a, Bdd.OR, d)))
# copy stack of rules
stack_copy = [list(i) for i in stack]
visited_copy = list(visited)
parsed_rule_copy = list(parsed_rule)
parsed_rule_copy[-1] = [parsed_rule_copy[-1][0], False, negate_bdd(parsed_rule_copy[-1][0].toBDD())]
if rule.action.is_chained():
_detect_anomaly(rule.action.chain, stack_copy, parsed_rule_copy, visited_copy, r, a, d,
result_queue, processed_id_rules, deep_search)
else:
# remove current stack rule
stack_copy.pop()
visited_copy.pop()
# special case RETURN in first ACL
if not visited_copy:
stack_copy = [[acl.rules[-1]]]
visited_copy.append(acl)
_detect_anomaly(visited_copy[-1], stack_copy, parsed_rule_copy, visited_copy, r, a, d,
result_queue, processed_id_rules, deep_search)
else: # rule is Permit or Deny
if rule.action.chain:
# accept = A ∪ (R ∩ P)
accept = synthesize(accept, Bdd.OR, synthesize(remain, Bdd.AND, rule.toBDD()))
else:
# d = D ∪ (R ∩ P)
deny = synthesize(deny, Bdd.OR, synthesize(remain, Bdd.AND, rule.toBDD()))
# remain = I ∩ ¬(a ∪ d)
remain = synthesize(Robdd.true(), Bdd.AND, negate_bdd(synthesize(accept, Bdd.OR, deny)))
stack.pop()
visited.pop()
def _detect_anomaly(rules, result_queue, processed_id_rules, deep_search):
"""Detect anomaly of the given rule set.
This algorithm is derived from the algorithm of Fireman.
It uses ROBDD to compare each rules.
The complexity of this algorithm is in O(n).
For more informations read :
- Firewall Policy Advisor for Anomaly Detection and rules analysis,
http://www.arc.uncc.edu/pubs/im03-cr.pdf
- FIREMAN : A Toolkit for FIREwall Modeling and ANalysis,
http://www.cs.ucdavis.edu/~su/publications/fireman.pdf
Parameters
----------
rules : Rule list. The list of rule to inspect
result_queue : multiprocessing Queue. A queue to put errors
"""
remain = Robdd.true()
accept = Robdd.false()
deny = Robdd.false()
error_list = deque()
error_list_append = error_list.append
for rule in rules:
processed_id_rules.put(rule.identifier)
error_rules = deque()
# Pj ⊆ Rj
if compare_bdd(rule.toBDD(), Bdd.IMPL, remain):
pass
else:
# Pj ∩ Rj = ∅
if not compare_bdd(rule.toBDD(), Bdd.AND, remain):
# Pj ⊆ Dj
if compare_bdd(rule.toBDD(), Bdd.IMPL, (deny if rule.action else accept)):
if deep_search:
for r in rules:
if r == rule:
break
# ∀ x < j, ∃ <Px, deny> such that Px ∩ Pj != ∅
if r.action != rule.action and compare_bdd(r.toBDD(), Bdd.AND, rule.toBDD()):
error_rules.append(r)
error_list_append(AnomalyError.error_message(ErrorType.INT_MASK_SHADOW, ErrorType.ERROR, rule, error_rules))
# Pj ∩ Dj = ∅
elif not compare_bdd(rule.toBDD(), Bdd.AND, (deny if rule.action else accept)):
if deep_search:
for r in rules:
if r == rule:
break
# ∀ x < j, ∃ <Px, accept> such that Px ∩ Pj != ∅
if r.action == rule.action and compare_bdd(r.toBDD(), Bdd.AND, rule.toBDD()):
error_rules.append(r)
error_list_append(AnomalyError.error_message(ErrorType.INT_MASK_REDUNDANT, ErrorType.ERROR, rule, error_rules))
else:
if deep_search:
for r in rules:
if r == rule:
break
# ∀ x < j, ∃ Px such that Px ∩ Pj != ∅
if compare_bdd(r.toBDD(), Bdd.AND, rule.toBDD()):
error_rules.append(r)
error_list_append(AnomalyError.error_message(ErrorType.INT_MASK_REDUNDANT_CORRELATION, ErrorType.ERROR, rule, error_rules))
else:
error_redudant = deque()
error_generalization = deque()
if deep_search:
# if deep search try to distinguish overlap of generalization or redundancy
for r in rules:
if r == rule:
break
# ∀ x < j, ∃ <Px, deny> such that Px ⊆ Pj
if r.action != rule.action and compare_bdd(r.toBDD(), Bdd.IMPL, rule.toBDD()):
error_generalization.append(r)
# ∀ x < j, ∃ <Px, accept> such that Px ⊆ Pj
elif r.action == rule.action and compare_bdd(r.toBDD(), Bdd.IMPL, rule.toBDD()):
error_redudant.append(r)
elif compare_bdd(r.toBDD(), Bdd.AND, rule.toBDD()):
error_rules.append(r)
if error_redudant:
error_list_append(AnomalyError.error_message(ErrorType.INT_PART_REDUNDANT, ErrorType.ERROR, rule, error_redudant))
if error_generalization:
error_list_append(AnomalyError.error_message(ErrorType.INT_PART_GENERALIZATION, ErrorType.WARNING, rule, error_generalization))
if error_rules:
error_list_append(AnomalyError.error_message(ErrorType.INT_PART_CORRELATION, ErrorType.WARNING, rule, error_rules))
else:
error_list_append(AnomalyError.error_message(ErrorType.INT_PART_CORRELATION, ErrorType.WARNING, rule, error_rules))
if rule.action:
accept = synthesize(accept, Bdd.OR, synthesize(remain, Bdd.AND, rule.toBDD()))
else:
deny = synthesize(deny, Bdd.OR, synthesize(remain, Bdd.AND, rule.toBDD()))
remain = synthesize(Robdd.true(), Bdd.AND, negate_bdd(synthesize(accept, Bdd.OR, deny)))
result_queue.put(error_list)
def is_subset(self, rule, test_rule):
return len(synthesize(test_rule.toBDD(), Bdd.IMPL, rule.toBDD()).items) <= 2
def is_subset(rule, test_rule):
"""return True if rule is a subset of test_rule, false otherwise (use ROBDD)"""
return len(synthesize(test_rule.toBDD(), Bdd.IMPL, rule.toBDD()).items) <= 2
def _detect_anomaly(rules, result_queue, processed_id_rules, deep_search):
"""Detect anomaly of the given rule set.
This algorithm is derived from the algorithm of Fireman.
It uses ROBDD to compare each rules.
The complexity of this algorithm is in O(n).
For more informations read :
- Firewall Policy Advisor for Anomaly Detection and rules analysis,
http://www.arc.uncc.edu/pubs/im03-cr.pdf
- FIREMAN : A Toolkit for FIREwall Modeling and ANalysis,
http://www.cs.ucdavis.edu/~su/publications/fireman.pdf
Parameters
----------
rules : Rule list. The list of rule to inspect
result_queue : multiprocessing Queue. A queue to put errors
"""
remain = Robdd.true()
accept = Robdd.false()
deny = Robdd.false()
error_list = deque()
error_list_append = error_list.append
for rule in rules:
processed_id_rules.put(rule.identifier)
error_rules = deque()
# Pj ⊆ Rj
if compare_bdd(rule.toBDD(), Bdd.IMPL, remain):
pass
else:
# Pj ∩ Rj = ∅
if not compare_bdd(rule.toBDD(), Bdd.AND, remain):
# Pj ⊆ Dj
if compare_bdd(rule.toBDD(), Bdd.IMPL,
(deny if rule.action else accept)):
if deep_search:
for r in rules:
if r == rule:
break
# ∀ x < j, ∃ <Px, deny> such that Px ∩ Pj != ∅
if r.action != rule.action and compare_bdd(
r.toBDD(), Bdd.AND, rule.toBDD()):
error_rules.append(r)
error_list_append(
AnomalyError.error_message(ErrorType.INT_MASK_SHADOW,
ErrorType.ERROR, rule,
error_rules))
# Pj ∩ Dj = ∅
elif not compare_bdd(rule.toBDD(), Bdd.AND,
(deny if rule.action else accept)):
if deep_search:
for r in rules:
if r == rule:
break
# ∀ x < j, ∃ <Px, accept> such that Px ∩ Pj != ∅
if r.action == rule.action and compare_bdd(
r.toBDD(), Bdd.AND, rule.toBDD()):
error_rules.append(r)
error_list_append(
AnomalyError.error_message(
ErrorType.INT_MASK_REDUNDANT, ErrorType.ERROR,
rule, error_rules))
else:
if deep_search:
for r in rules:
if r == rule:
break
# ∀ x < j, ∃ Px such that Px ∩ Pj != ∅
if compare_bdd(r.toBDD(), Bdd.AND, rule.toBDD()):
error_rules.append(r)
error_list_append(
AnomalyError.error_message(
ErrorType.INT_MASK_REDUNDANT_CORRELATION,
ErrorType.ERROR, rule, error_rules))
else:
error_redudant = deque()
error_generalization = deque()
if deep_search:
# if deep search try to distinguish overlap of generalization or redundancy
for r in rules:
if r == rule:
break
# ∀ x < j, ∃ <Px, deny> such that Px ⊆ Pj
if r.action != rule.action and compare_bdd(
r.toBDD(), Bdd.IMPL, rule.toBDD()):
error_generalization.append(r)
# ∀ x < j, ∃ <Px, accept> such that Px ⊆ Pj
elif r.action == rule.action and compare_bdd(
r.toBDD(), Bdd.IMPL, rule.toBDD()):
error_redudant.append(r)
elif compare_bdd(r.toBDD(), Bdd.AND, rule.toBDD()):
error_rules.append(r)
if error_redudant:
error_list_append(
AnomalyError.error_message(
ErrorType.INT_PART_REDUNDANT, ErrorType.ERROR,
rule, error_redudant))
if error_generalization:
error_list_append(
AnomalyError.error_message(
ErrorType.INT_PART_GENERALIZATION,
ErrorType.WARNING, rule, error_generalization))
if error_rules:
error_list_append(
AnomalyError.error_message(
ErrorType.INT_PART_CORRELATION,
ErrorType.WARNING, rule, error_rules))
else:
error_list_append(
AnomalyError.error_message(
ErrorType.INT_PART_CORRELATION, ErrorType.WARNING,
rule, error_rules))
if rule.action:
accept = synthesize(accept, Bdd.OR,
synthesize(remain, Bdd.AND, rule.toBDD()))
else:
deny = synthesize(deny, Bdd.OR,
synthesize(remain, Bdd.AND, rule.toBDD()))
remain = synthesize(Robdd.true(), Bdd.AND,
negate_bdd(synthesize(accept, Bdd.OR, deny)))
result_queue.put(error_list)
def _detect_anomaly(
acl, stack, parsed_rule, visited, remain, accept, deny, result_queue, processed_id_rules, deep_search
):
"""Recursive algorithm for ACL graph traversal.
This recursive algorithm construct the list of all possible list of rule for the ACL chained model.
Then it launch the _classify_anomaly function on each rule and try to detect anomaly if any.
Parameters
----------
acl : ACL. The acl to inspect.
stack : list of Rule list. A stack of rule list.
parsed_rule : Rule list. A list of parse rule.
visisted : ACL list. List of visited ACL.
remain : ROBDD. Remain ROBDD.
accept : ROBDD. Accept ROBDD.
deny : ROBDD. Deny ROBDD.
result_queue : list. List of error.
processed_id_rules : list. List of processed rules.
deep_search : Bool. If true find blamed rules.
"""
if acl not in visited:
visited.append(acl)
stack.append(list(acl.rules))
while stack:
rule_list = stack[-1]
while rule_list:
rule = rule_list.pop(0)
processed_id_rules.put(rule.identifier)
_classify_anomaly(rule, parsed_rule, remain, accept, deny, result_queue, deep_search)
parsed_rule.append([rule, rule.action.chain, rule.toBDD()])
if rule.action.is_chained() or rule.action.is_return():
# a = accept & True -> copy of accept
a = synthesize(accept, Bdd.AND, Robdd.true())
# d = D ∪ (R ∩ ¬P)
d = synthesize(deny, Bdd.OR, synthesize(remain, Bdd.AND, negate_bdd(rule.toBDD())))
# r = I ∩ ¬(a ∪ d)
r = synthesize(Robdd.true(), Bdd.AND, negate_bdd(synthesize(a, Bdd.OR, d)))
# copy stack of rules
stack_copy = [list(i) for i in stack]
visited_copy = list(visited)
parsed_rule_copy = list(parsed_rule)
parsed_rule_copy[-1] = [parsed_rule_copy[-1][0], False, negate_bdd(parsed_rule_copy[-1][0].toBDD())]
if rule.action.is_chained():
_detect_anomaly(
rule.action.chain,
stack_copy,
parsed_rule_copy,
visited_copy,
r,
a,
d,
result_queue,
processed_id_rules,
deep_search,
)
else:
# remove current stack rule
stack_copy.pop()
visited_copy.pop()
# special case RETURN in first ACL
if not visited_copy:
stack_copy = [[acl.rules[-1]]]
visited_copy.append(acl)
_detect_anomaly(
visited_copy[-1],
stack_copy,
parsed_rule_copy,
visited_copy,
r,
a,
d,
result_queue,
processed_id_rules,
deep_search,
)
else: # rule is Permit or Deny
if rule.action.chain:
# accept = A ∪ (R ∩ P)
accept = synthesize(accept, Bdd.OR, synthesize(remain, Bdd.AND, rule.toBDD()))
else:
# d = D ∪ (R ∩ P)
deny = synthesize(deny, Bdd.OR, synthesize(remain, Bdd.AND, rule.toBDD()))
# remain = I ∩ ¬(a ∪ d)
remain = synthesize(Robdd.true(), Bdd.AND, negate_bdd(synthesize(accept, Bdd.OR, deny)))
stack.pop()
visited.pop()
def _distributed_detection(self, acl_list, remain, tree_path):
"""Detection method for a given acl with a given remain ROBDD.
This algorithm is derived from the algorithm of Fireman.
For more informations read :
- Firewall Policy Advisor for Anomaly Detection and rules analysis,
http://www.arc.uncc.edu/pubs/im03-cr.pdf
- FIREMAN : A Toolkit for FIREwall Modeling and ANalysis,
http://www.cs.ucdavis.edu/~su/publications/fireman.pdf
Parameters
----------
acl : Rule list. The rule list to test
remain : ROBDD. The remaining ROBDD
Return
------
Return a tuple of remaining rules and the list error found in this context
"""
accept_robdd_list = []
error_list = deque()
error_list_append = error_list.append
for acl in acl_list:
for rule_path in acl.get_rules_path():
accept = Robdd.false()
deny = Robdd.false()
if self.cancel:
break
for rule, action in rule_path:
if self.cancel:
break
Gtk.Gtk_Main.Gtk_Main().update_progress_bar(1)
Gtk.Gtk_Main.Gtk_Main().update_interface()
error_rules = []
rule_action = rule.action.chain if isinstance(
rule.action.chain, bool) else action
if rule_action:
# P ⊆ I
if compare_bdd(rule.toBDD(), Bdd.IMPL, remain):
pass
# P ⊆ ¬I
elif compare_bdd(rule.toBDD(), Bdd.IMPL,
negate_bdd(remain)):
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx, ∃ <Px, deny> such that Px ∩ Pj != ∅
error_rules = self.search_rules(
rule, Bdd.AND, False, tree_path)
error_list_append(
AnomalyError.error_message(
ErrorType.DIST_SHADOW, ErrorType.ERROR,
rule, error_rules))
# P ∩ I != ∅
else:
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx, ∃ Px such that Px ∩ Pj != ∅
error_rules = self.search_rules(
rule, Bdd.AND, None, tree_path)
error_list_append(
AnomalyError.error_message(
ErrorType.DIST_CORRELATE,
ErrorType.WARNING, rule, error_rules))
else:
# P ⊆ I
if compare_bdd(rule.toBDD(), Bdd.IMPL, remain):
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx, ∃ <Px, accept> such that Px ∩ Pj != ∅
error_rules = self.search_rules(
rule, Bdd.AND, True, tree_path)
error_list_append(
AnomalyError.error_message(
ErrorType.DIST_RAISED, ErrorType.WARNING,
rule, error_rules))
# P ⊆ ¬I
elif compare_bdd(rule.toBDD(), Bdd.IMPL,
negate_bdd(remain)):
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx, ∃ <Px, deny> such that Px ∩ Pj != ∅
error_rules = self.search_rules(
rule, Bdd.AND, False, tree_path)
error_list_append(
AnomalyError.error_message(
ErrorType.DIST_REDUNDANT,
ErrorType.WARNING, rule, error_rules))
# P ∩ I != ∅
else:
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx such that Px ∩ Pj != ∅
error_rules = self.search_rules(
rule, Bdd.AND, None, tree_path)
error_list_append(
AnomalyError.error_message(
ErrorType.DIST_CORRELATE,
ErrorType.WARNING, rule, error_rules))
# update value
if rule.action.is_chained() or rule.action.is_return():
if action:
# D = D ∪ ¬(A ∪ P) = D ∪ (¬A ∩ ¬P)
deny = synthesize(
deny, Bdd.OR,
negate_bdd(
synthesize(accept, Bdd.OR, rule.toBDD())))
else:
# D = D ∪ (¬A ∩ P)
deny = synthesize(
deny, Bdd.OR,
synthesize(negate_bdd(accept), Bdd.AND,
rule.toBDD()))
else:
if rule.action.chain:
# A = A ∪ (¬D ∩ P)
accept = synthesize(
accept, Bdd.OR,
synthesize(negate_bdd(deny), Bdd.AND,
rule.toBDD()))
else:
# D = D ∪ (¬A ∩ P)
deny = synthesize(
deny, Bdd.OR,
synthesize(negate_bdd(accept), Bdd.AND,
rule.toBDD()))
accept_robdd_list.append(accept)
res_accept = Robdd.false()
for a in accept_robdd_list:
res_accept = synthesize(res_accept, Bdd.OR, a)
return res_accept, error_list
def _distributed_detection(self, acl_list, remain, tree_path):
"""Detection method for a given acl with a given remain ROBDD.
This algorithm is derived from the algorithm of Fireman.
For more informations read :
- Firewall Policy Advisor for Anomaly Detection and rules analysis,
http://www.arc.uncc.edu/pubs/im03-cr.pdf
- FIREMAN : A Toolkit for FIREwall Modeling and ANalysis,
http://www.cs.ucdavis.edu/~su/publications/fireman.pdf
Parameters
----------
acl : Rule list. The rule list to test
remain : ROBDD. The remaining ROBDD
Return
------
Return a tuple of remaining rules and the list error found in this context
"""
accept_robdd_list = []
error_list = deque()
error_list_append = error_list.append
for acl in acl_list:
for rule_path in acl.get_rules_path():
accept = Robdd.false()
deny = Robdd.false()
if self.cancel:
break
for rule, action in rule_path:
if self.cancel:
break
Gtk.Gtk_Main.Gtk_Main().update_progress_bar(1)
Gtk.Gtk_Main.Gtk_Main().update_interface()
error_rules = []
rule_action = rule.action.chain if isinstance(rule.action.chain, bool) else action
if rule_action:
# P ⊆ I
if compare_bdd(rule.toBDD(), Bdd.IMPL, remain):
pass
# P ⊆ ¬I
elif compare_bdd(rule.toBDD(), Bdd.IMPL, negate_bdd(remain)):
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx, ∃ <Px, deny> such that Px ∩ Pj != ∅
error_rules = self.search_rules(rule, Bdd.AND, False, tree_path)
error_list_append(
AnomalyError.error_message(ErrorType.DIST_SHADOW, ErrorType.ERROR, rule, error_rules))
# P ∩ I != ∅
else:
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx, ∃ Px such that Px ∩ Pj != ∅
error_rules = self.search_rules(rule, Bdd.AND, None, tree_path)
error_list_append(
AnomalyError.error_message(ErrorType.DIST_CORRELATE, ErrorType.WARNING, rule, error_rules))
else:
# P ⊆ I
if compare_bdd(rule.toBDD(), Bdd.IMPL, remain):
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx, ∃ <Px, accept> such that Px ∩ Pj != ∅
error_rules = self.search_rules(rule, Bdd.AND, True, tree_path)
error_list_append(
AnomalyError.error_message(ErrorType.DIST_RAISED, ErrorType.WARNING, rule, error_rules))
# P ⊆ ¬I
elif compare_bdd(rule.toBDD(), Bdd.IMPL, negate_bdd(remain)):
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx, ∃ <Px, deny> such that Px ∩ Pj != ∅
error_rules = self.search_rules(rule, Bdd.AND, False, tree_path)
error_list_append(
AnomalyError.error_message(ErrorType.DIST_REDUNDANT, ErrorType.WARNING, rule, error_rules))
# P ∩ I != ∅
else:
if self.deep_search:
# ∀ ACLx < ACLj, ∀ x ∈ ACLx such that Px ∩ Pj != ∅
error_rules = self.search_rules(rule, Bdd.AND, None, tree_path)
error_list_append(
AnomalyError.error_message(ErrorType.DIST_CORRELATE, ErrorType.WARNING, rule, error_rules))
# update value
if rule.action.is_chained() or rule.action.is_return():
if action:
# D = D ∪ ¬(A ∪ P) = D ∪ (¬A ∩ ¬P)
deny = synthesize(deny, Bdd.OR, negate_bdd(synthesize(accept, Bdd.OR, rule.toBDD())))
else:
# D = D ∪ (¬A ∩ P)
deny = synthesize(deny, Bdd.OR, synthesize(negate_bdd(accept), Bdd.AND, rule.toBDD()))
else:
if rule.action.chain:
# A = A ∪ (¬D ∩ P)
accept = synthesize(accept, Bdd.OR, synthesize(negate_bdd(deny), Bdd.AND, rule.toBDD()))
else:
# D = D ∪ (¬A ∩ P)
deny = synthesize(deny, Bdd.OR, synthesize(negate_bdd(accept), Bdd.AND, rule.toBDD()))
accept_robdd_list.append(accept)
res_accept = Robdd.false()
for a in accept_robdd_list:
res_accept = synthesize(res_accept, Bdd.OR, a)
return res_accept, error_list