mirror of
https://github.com/ail-project/ail-framework.git
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208 lines
7.9 KiB
Python
Executable file
208 lines
7.9 KiB
Python
Executable file
#!/usr/bin/env python3
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# -*-coding:UTF-8 -*
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import os
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import sys
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sys.path.append(os.environ['AIL_BIN'])
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##################################
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# Import Project packages
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##################################
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from lib.ConfigLoader import ConfigLoader
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config_loader = ConfigLoader()
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r_metadata = config_loader.get_db_conn("Kvrocks_Correlations")
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config_loader = None
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##################################
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# CORRELATION MIGRATION
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##################################
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#
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# MIGRATE TO KVROCKS + Rename correlation Keys
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# => Add support for correlations between subtypes
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# => Common correlation engine for each objects
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#
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# Objects Iterations: -screenshot
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# -decoded
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# -subtypes
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# -domains
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#
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# /!\ Handle reinsertion /!\
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#
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#
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# CORRELATION DB ????? => purge if needed
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#
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#
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#
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#
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#
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##################################
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# CORRELATION MIGRATION
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##################################
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CORRELATION_TYPES_BY_OBJ = {
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"cookie-name": ["domain"],
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"cryptocurrency": ["domain", "item"],
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"cve": ["domain", "item"],
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"decoded": ["domain", "item"],
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"domain": ["cve", "cookie-name", "cryptocurrency", "decoded", "favicon", "item", "pgp", "title", "screenshot", "username"],
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"favicon": ["domain", "item"], # TODO Decoded
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"item": ["cve", "cryptocurrency", "decoded", "domain", "favicon", "pgp", "screenshot", "title", "username"],
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"pgp": ["domain", "item"],
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"screenshot": ["domain", "item"],
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"title": ["domain", "item"],
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"username": ["domain", "item"],
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}
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def get_obj_correl_types(obj_type):
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return CORRELATION_TYPES_BY_OBJ.get(obj_type)
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def sanityze_obj_correl_types(obj_type, correl_types):
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obj_correl_types = get_obj_correl_types(obj_type)
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if correl_types:
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correl_types = set(correl_types).intersection(obj_correl_types)
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if not correl_types:
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correl_types = obj_correl_types
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return correl_types
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def get_nb_correlation_by_correl_type(obj_type, subtype, obj_id, correl_type):
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return r_metadata.scard(f'correlation:obj:{obj_type}:{subtype}:{correl_type}:{obj_id}')
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def get_nb_correlations(obj_type, subtype, obj_id, filter_types=[]):
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if subtype is None:
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subtype = ''
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obj_correlations = {}
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filter_types = sanityze_obj_correl_types(obj_type, filter_types)
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for correl_type in filter_types:
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obj_correlations[correl_type] = get_nb_correlation_by_correl_type(obj_type, subtype, obj_id, correl_type)
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return obj_correlations
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def get_correlation_by_correl_type(obj_type, subtype, obj_id, correl_type, unpack=False):
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correl = r_metadata.smembers(f'correlation:obj:{obj_type}:{subtype}:{correl_type}:{obj_id}')
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if unpack:
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unpacked = []
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for str_correl in correl:
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unpacked.append(str_correl.split(':', 1))
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return unpacked
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else:
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return correl
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def get_correlations(obj_type, subtype, obj_id, filter_types=[], unpack=False):
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if subtype is None:
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subtype = ''
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obj_correlations = {}
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filter_types = sanityze_obj_correl_types(obj_type, filter_types)
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for correl_type in filter_types:
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obj_correlations[correl_type] = get_correlation_by_correl_type(obj_type, subtype, obj_id, correl_type,
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unpack=unpack)
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return obj_correlations
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def exists_obj_correlation(obj_type, subtype, obj_id, obj2_type):
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if subtype is None:
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subtype = ''
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return r_metadata.exists(f'correlation:obj:{obj_type}:{subtype}:{obj2_type}:{obj_id}')
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def is_obj_correlated(obj_type, subtype, obj_id, obj2_type, subtype2, obj2_id):
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if subtype is None:
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subtype = ''
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if subtype2 is None:
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subtype2 = ''
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try:
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return r_metadata.sismember(f'correlation:obj:{obj_type}:{subtype}:{obj2_type}:{obj_id}', f'{subtype2}:{obj2_id}')
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except:
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return False
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def add_obj_correlation(obj1_type, subtype1, obj1_id, obj2_type, subtype2, obj2_id):
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if subtype1 is None:
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subtype1 = ''
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if subtype2 is None:
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subtype2 = ''
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r_metadata.sadd(f'correlation:obj:{obj1_type}:{subtype1}:{obj2_type}:{obj1_id}', f'{subtype2}:{obj2_id}')
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r_metadata.sadd(f'correlation:obj:{obj2_type}:{subtype2}:{obj1_type}:{obj2_id}', f'{subtype1}:{obj1_id}')
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def delete_obj_correlation(obj1_type, subtype1, obj1_id, obj2_type, subtype2, obj2_id):
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if subtype1 is None:
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subtype1 = ''
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if subtype2 is None:
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subtype2 = ''
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r_metadata.srem(f'correlation:obj:{obj1_type}:{subtype1}:{obj2_type}:{obj1_id}', f'{subtype2}:{obj2_id}')
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r_metadata.srem(f'correlation:obj:{obj2_type}:{subtype2}:{obj1_type}:{obj2_id}', f'{subtype1}:{obj1_id}')
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def delete_obj_correlations(obj_type, subtype, obj_id):
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obj_correlations = get_correlations(obj_type, subtype, obj_id)
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for correl_type in obj_correlations:
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for str_obj in obj_correlations[correl_type]:
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subtype2, obj2_id = str_obj.split(':', 1)
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delete_obj_correlation(obj_type, subtype, obj_id, correl_type, subtype2, obj2_id)
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# # bypass max result/objects ???
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# def get_correlation_depht(obj_type, subtype, obj_id, filter_types=[], level=1, nb_max=300):
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# objs = set()
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# _get_correlation_depht(objs, obj_type, subtype, obj_id, filter_types, level, nb_max)
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# return objs
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#
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# def _get_correlation_depht(objs, obj_type, subtype, obj_id, filter_types, level, nb_max, previous_str_obj=''):
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# obj_str_id = get_obj_str_id(obj_type, subtype, obj_id)
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# objs.add(obj_str_id)
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#
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# obj_correlations = get_correlations(obj_type, subtype, obj_id, filter_types=filter_types)
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# for correl_type in obj_correlations:
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# for str_obj in obj_correlations[correl_type]:
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# subtype2, obj2_id = str_obj.split(':', 1)
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# obj2_str_id = get_obj_str_id(correl_type, subtype2, obj2_id)
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#
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# if obj2_str_id == previous_str_obj:
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# continue
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#
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# if len(nodes) > nb_max:
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# break
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# objs.add(obj2_str_id)
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#
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# if level > 0:
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# next_level = level - 1
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# _get_correlation_depht(objs, correl_type, subtype2, obj2_id, filter_types, next_level, nb_max,
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# previous_str_obj=obj_str_id)
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def get_obj_str_id(obj_type, subtype, obj_id):
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if subtype is None:
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subtype = ''
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return f'{obj_type}:{subtype}:{obj_id}'
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def get_correlations_graph_nodes_links(obj_type, subtype, obj_id, filter_types=[], max_nodes=300, level=1, flask_context=False):
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links = set()
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nodes = set()
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meta = {'complete': True, 'objs': set()}
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obj_str_id = get_obj_str_id(obj_type, subtype, obj_id)
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_get_correlations_graph_node(links, nodes, meta, obj_type, subtype, obj_id, level, max_nodes, filter_types=filter_types, previous_str_obj='')
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return obj_str_id, nodes, links, meta
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def _get_correlations_graph_node(links, nodes, meta, obj_type, subtype, obj_id, level, max_nodes, filter_types=[], previous_str_obj=''):
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obj_str_id = get_obj_str_id(obj_type, subtype, obj_id)
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meta['objs'].add(obj_str_id)
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nodes.add(obj_str_id)
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obj_correlations = get_correlations(obj_type, subtype, obj_id, filter_types=filter_types)
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# print(obj_correlations)
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for correl_type in obj_correlations:
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for str_obj in obj_correlations[correl_type]:
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subtype2, obj2_id = str_obj.split(':', 1)
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obj2_str_id = get_obj_str_id(correl_type, subtype2, obj2_id)
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meta['objs'].add(obj2_str_id)
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if obj2_str_id == previous_str_obj:
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continue
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if len(nodes) > max_nodes != 0:
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meta['complete'] = False
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break
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nodes.add(obj2_str_id)
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links.add((obj_str_id, obj2_str_id))
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if level > 0:
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next_level = level - 1
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_get_correlations_graph_node(links, nodes, meta, correl_type, subtype2, obj2_id, next_level, max_nodes, filter_types=filter_types, previous_str_obj=obj_str_id)
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