Generalize checking functionality within environment.py and add prop_scores dict so all scoring info is one python object
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387810c4a3
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f5d199bedf
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@ -218,55 +218,46 @@ class Environment(DataStoreMixin):
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# default weights used for the semantic equivalence process
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weights = {
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"attack-pattern": {
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"name": 30,
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"external_references": 70,
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"method": _attack_pattern_checks,
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"name": (30, partial_string_based),
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"external_references": (70, partial_external_reference_based),
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},
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"campaign": {
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"name": 60,
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"aliases": 40,
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"method": _campaign_checks,
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"name": (60, partial_string_based),
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"aliases": (40, partial_list_based),
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},
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"identity": {
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"name": 60,
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"identity_class": 20,
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"sectors": 20,
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"method": _identity_checks,
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"name": (60, partial_string_based),
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"identity_class": (20, exact_match),
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"sectors": (20, partial_list_based),
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},
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"indicator": {
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"indicator_types": 15,
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"pattern": 80,
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"valid_from": 5,
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"indicator_types": (15, partial_list_based),
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"pattern": (80, custom_pattern_based),
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"valid_from": (5, partial_timestamp_based),
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"tdelta": 1, # One day interval
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"method": _indicator_checks,
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},
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"location": {
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"longitude_latitude": 34,
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"region": 33,
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"country": 33,
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"longitude_latitude": (34, partial_location_distance),
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"region": (33, exact_match),
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"country": (33, exact_match),
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"threshold": 1000.0,
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"method": _location_checks,
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},
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"malware": {
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"malware_types": 20,
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"name": 80,
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"method": _malware_checks,
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"malware_types": (20, partial_list_based),
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"name": (80, partial_string_based),
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},
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"threat-actor": {
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"name": 60,
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"threat_actor_types": 20,
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"aliases": 20,
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"method": _threat_actor_checks,
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"name": (60, partial_string_based),
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"threat_actor_types": (20, partial_list_based),
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"aliases": (20, partial_list_based),
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},
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"tool": {
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"tool_types": 20,
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"name": 80,
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"method": _tool_checks,
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"tool_types": (20, partial_list_based),
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"name": (80, partial_string_based),
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},
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"vulnerability": {
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"name": 30,
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"external_references": 70,
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"method": _vulnerability_checks,
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"name": (30, partial_string_based),
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"external_references": (70, partial_external_reference_based),
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},
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"_internal": {
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"ignore_spec_version": False,
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@ -287,9 +278,38 @@ class Environment(DataStoreMixin):
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try:
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method = weights[type1]["method"]
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except KeyError:
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try:
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weights[type1]
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except KeyError:
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logger.warning("'%s' type has no semantic equivalence method to call!", type1)
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sum_weights = matching_score = 0
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else:
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matching_score = 0.0
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sum_weights = 0.0
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prop_scores = {}
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for prop in weights[type1]:
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if check_property_present(prop, obj1, obj2) or prop == "longitude_latitude":
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w = weights[type1][prop][0]
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comp_funct = weights[type1][prop][1]
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if comp_funct == partial_timestamp_based:
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contributing_score = w * comp_funct(obj1[prop], obj2[prop], weights[type1]["tdelta"])
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elif comp_funct == partial_location_distance:
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threshold = weights[type1]["threshold"]
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contributing_score = w * comp_funct(obj1["latitude"], obj1["longitude"], obj2["latitude"], obj2["longitude"], threshold)
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else:
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contributing_score = w * comp_funct(obj1[prop], obj2[prop])
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sum_weights += w
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matching_score += contributing_score
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prop_scores[prop] = (w, contributing_score)
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logger.debug("'%s' check -- weight: %s, contributing score: %s", prop, w, contributing_score)
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prop_scores["matching_score"] = matching_score
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prop_scores["sum_weights"] = sum_weights
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else:
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logger.debug("Starting semantic equivalence process between: '%s' and '%s'", obj1["id"], obj2["id"])
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matching_score, sum_weights = method(obj1, obj2, **weights[type1])
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