# -*- coding: utf-8 -*-
from collections import OrderedDict
from elasticsearch_dsl.search import Q
from elasticsearch_dsl import connections, Object, Document, Index, Nested, \
InnerDoc, Integer, Text, Boolean, Ip, Date, Search
from elasticsearch.helpers import reindex
from parsedmarc.log import logger
from parsedmarc.utils import human_timestamp_to_datetime
from parsedmarc import InvalidForensicReport
[docs]class ElasticsearchError(Exception):
"""Raised when an Elasticsearch error occurs"""
class _PolicyOverride(InnerDoc):
type = Text()
comment = Text()
class _PublishedPolicy(InnerDoc):
domain = Text()
adkim = Text()
aspf = Text()
p = Text()
sp = Text()
pct = Integer()
fo = Text()
class _DKIMResult(InnerDoc):
domain = Text()
selector = Text()
result = Text()
class _SPFResult(InnerDoc):
domain = Text()
scope = Text()
results = Text()
class _AggregateReportDoc(Document):
class Index:
name = "dmarc_aggregate"
xml_schema = Text()
org_name = Text()
org_email = Text()
org_extra_contact_info = Text()
report_id = Text()
date_range = Date()
date_begin = Date()
date_end = Date()
errors = Text()
published_policy = Object(_PublishedPolicy)
source_ip_address = Ip()
source_country = Text()
source_reverse_dns = Text()
source_Base_domain = Text()
message_count = Integer
disposition = Text()
dkim_aligned = Boolean()
spf_aligned = Boolean()
passed_dmarc = Boolean()
policy_overrides = Nested(_PolicyOverride)
header_from = Text()
envelope_from = Text()
envelope_to = Text()
dkim_results = Nested(_DKIMResult)
spf_results = Nested(_SPFResult)
def add_policy_override(self, type_, comment):
self.policy_overrides.append(_PolicyOverride(type=type_,
comment=comment))
def add_dkim_result(self, domain, selector, result):
self.dkim_results.append(_DKIMResult(domain=domain,
selector=selector,
result=result))
def add_spf_result(self, domain, scope, result):
self.spf_results.append(_SPFResult(domain=domain,
scope=scope,
result=result))
def save(self, ** kwargs):
self.passed_dmarc = False
self.passed_dmarc = self.spf_aligned or self.dkim_aligned
return super().save(** kwargs)
class _EmailAddressDoc(InnerDoc):
display_name = Text()
address = Text()
class _EmailAttachmentDoc(Document):
filename = Text()
content_type = Text()
sha256 = Text()
class _ForensicSampleDoc(InnerDoc):
raw = Text()
headers = Object()
headers_only = Boolean()
to = Nested(_EmailAddressDoc)
subject = Text()
filename_safe_subject = Text()
_from = Object(_EmailAddressDoc)
date = Date()
reply_to = Nested(_EmailAddressDoc)
cc = Nested(_EmailAddressDoc)
bcc = Nested(_EmailAddressDoc)
body = Text()
attachments = Nested(_EmailAttachmentDoc)
def add_to(self, display_name, address):
self.to.append(_EmailAddressDoc(display_name=display_name,
address=address))
def add_reply_to(self, display_name, address):
self.reply_to.append(_EmailAddressDoc(display_name=display_name,
address=address))
def add_cc(self, display_name, address):
self.cc.append(_EmailAddressDoc(display_name=display_name,
address=address))
def add_bcc(self, display_name, address):
self.bcc.append(_EmailAddressDoc(display_name=display_name,
address=address))
def add_attachment(self, filename, content_type, sha256):
self.attachments.append(_EmailAttachmentDoc(filename=filename,
content_type=content_type, sha256=sha256))
class _ForensicReportDoc(Document):
class Index:
name = "dmarc_forensic"
feedback_type = Text()
user_agent = Text()
version = Text()
original_mail_from = Text()
arrival_date = Date()
domain = Text()
original_envelope_id = Text()
authentication_results = Text()
delivery_results = Text()
source_ip_address = Ip()
source_country = Text()
source_reverse_dns = Text()
source_authentication_mechanisms = Text()
source_auth_failures = Text()
dkim_domain = Text()
original_rcpt_to = Text()
sample = Object(_ForensicSampleDoc)
[docs]class AlreadySaved(ValueError):
"""Raised when a report to be saved matches an existing report"""
[docs]def set_hosts(hosts, use_ssl=False, ssl_cert_path=None,
username=None, password=None, timeout=60.0):
"""
Sets the Elasticsearch hosts to use
Args:
hosts (str): A single hostname or URL, or list of hostnames or URLs
use_ssl (bool): Use a HTTPS connection to the server
ssl_cert_path (str): Path to the certificate chain
username (str): The username to use for authentication
password (str): The password to use for authentication
timeout (float): Timeout in seconds
"""
if type(hosts) != list:
hosts = [hosts]
conn_params = {
"hosts": hosts,
"timeout": timeout
}
if use_ssl:
conn_params['use_ssl'] = True
if ssl_cert_path:
conn_params['verify_certs'] = True
conn_params['ca_certs'] = ssl_cert_path
else:
conn_params['verify_certs'] = False
if username:
conn_params['http_auth'] = (username+":"+password)
connections.create_connection(**conn_params)
[docs]def create_indexes(names, settings=None):
"""
Create Elasticsearch indexes
Args:
names (list): A list of index names
settings (dict): Index settings
"""
for name in names:
index = Index(name)
try:
if not index.exists():
logger.debug("Creating Elasticsearch index: {0}".format(name))
if settings is None:
index.settings(number_of_shards=1,
number_of_replicas=0)
else:
index.settings(**settings)
index.create()
except Exception as e:
raise ElasticsearchError(
"Elasticsearch error: {0}".format(e.__str__()))
[docs]def migrate_indexes(aggregate_indexes=None, forensic_indexes=None):
"""
Updates index mappings
Args:
aggregate_indexes (list): A list of aggregate index names
forensic_indexes (list): A list of forensic index names
"""
version = 2
if aggregate_indexes is None:
aggregate_indexes = []
if forensic_indexes is None:
forensic_indexes = []
for aggregate_index_name in aggregate_indexes:
if not Index(aggregate_index_name).exists():
continue
aggregate_index = Index(aggregate_index_name)
doc = "doc"
fo_field = "published_policy.fo"
fo = "fo"
fo_mapping = aggregate_index.get_field_mapping(fields=[fo_field])
fo_mapping = fo_mapping[list(fo_mapping.keys())[0]]["mappings"]
if doc not in fo_mapping:
continue
fo_mapping = fo_mapping[doc][fo_field]["mapping"][fo]
fo_type = fo_mapping["type"]
if fo_type == "long":
new_index_name = "{0}-v{1}".format(aggregate_index_name, version)
body = {"properties": {"published_policy.fo": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
}
Index(new_index_name).create()
Index(new_index_name).put_mapping(doc_type=doc, body=body)
reindex(connections.get_connection(), aggregate_index_name,
new_index_name)
Index(aggregate_index_name).delete()
for forensic_index in forensic_indexes:
pass
[docs]def save_aggregate_report_to_elasticsearch(aggregate_report,
index_suffix=None,
monthly_indexes=False,
number_of_shards=1,
number_of_replicas=0):
"""
Saves a parsed DMARC aggregate report to ElasticSearch
Args:
aggregate_report (OrderedDict): A parsed forensic report
index_suffix (str): The suffix of the name of the index to save to
monthly_indexes (bool): Use monthly indexes instead of daily indexes
number_of_shards (int): The number of shards to use in the index
number_of_replicas (int): The number of replicas to use in the index
Raises:
AlreadySaved
"""
logger.info("Saving aggregate report to Elasticsearch")
aggregate_report = aggregate_report.copy()
metadata = aggregate_report["report_metadata"]
org_name = metadata["org_name"]
report_id = metadata["report_id"]
domain = aggregate_report["policy_published"]["domain"]
begin_date = human_timestamp_to_datetime(metadata["begin_date"],
to_utc=True)
end_date = human_timestamp_to_datetime(metadata["end_date"],
to_utc=True)
begin_date_human = begin_date.strftime("%Y-%m-%d %H:%M:%SZ")
end_date_human = end_date.strftime("%Y-%m-%d %H:%M:%SZ")
if monthly_indexes:
index_date = begin_date.strftime("%Y-%m")
else:
index_date = begin_date.strftime("%Y-%m-%d")
aggregate_report["begin_date"] = begin_date
aggregate_report["end_date"] = end_date
date_range = [aggregate_report["begin_date"],
aggregate_report["end_date"]]
org_name_query = Q(dict(match_phrase=dict(org_name=org_name)))
report_id_query = Q(dict(match_phrase=dict(report_id=report_id)))
domain_query = Q(dict(match_phrase={"published_policy.domain": domain}))
begin_date_query = Q(dict(match=dict(date_begin=begin_date)))
end_date_query = Q(dict(match=dict(date_end=end_date)))
if index_suffix is not None:
search = Search(index="dmarc_aggregate_{0}*".format(index_suffix))
else:
search = Search(index="dmarc_aggregate*")
query = org_name_query & report_id_query & domain_query
query = query & begin_date_query & end_date_query
search.query = query
existing = search.execute()
if len(existing) > 0:
raise AlreadySaved("An aggregate report ID {0} from {1} about {2} "
"with a date range of {3} UTC to {4} UTC already "
"exists in "
"Elasticsearch".format(report_id,
org_name,
domain,
begin_date_human,
end_date_human))
published_policy = _PublishedPolicy(
domain=aggregate_report["policy_published"]["domain"],
adkim=aggregate_report["policy_published"]["adkim"],
aspf=aggregate_report["policy_published"]["aspf"],
p=aggregate_report["policy_published"]["p"],
sp=aggregate_report["policy_published"]["sp"],
pct=aggregate_report["policy_published"]["pct"],
fo=aggregate_report["policy_published"]["fo"]
)
for record in aggregate_report["records"]:
agg_doc = _AggregateReportDoc(
xml_schema=aggregate_report["xml_schema"],
org_name=metadata["org_name"],
org_email=metadata["org_email"],
org_extra_contact_info=metadata["org_extra_contact_info"],
report_id=metadata["report_id"],
date_range=date_range,
date_begin=aggregate_report["begin_date"],
date_end=aggregate_report["end_date"],
errors=metadata["errors"],
published_policy=published_policy,
source_ip_address=record["source"]["ip_address"],
source_country=record["source"]["country"],
source_reverse_dns=record["source"]["reverse_dns"],
source_base_domain=record["source"]["base_domain"],
message_count=record["count"],
disposition=record["policy_evaluated"]["disposition"],
dkim_aligned=record["policy_evaluated"]["dkim"] is not None and
record["policy_evaluated"]["dkim"].lower() == "pass",
spf_aligned=record["policy_evaluated"]["spf"] is not None and
record["policy_evaluated"]["spf"].lower() == "pass",
header_from=record["identifiers"]["header_from"],
envelope_from=record["identifiers"]["envelope_from"],
envelope_to=record["identifiers"]["envelope_to"]
)
for override in record["policy_evaluated"]["policy_override_reasons"]:
agg_doc.add_policy_override(type_=override["type"],
comment=override["comment"])
for dkim_result in record["auth_results"]["dkim"]:
agg_doc.add_dkim_result(domain=dkim_result["domain"],
selector=dkim_result["selector"],
result=dkim_result["result"])
for spf_result in record["auth_results"]["spf"]:
agg_doc.add_spf_result(domain=spf_result["domain"],
scope=spf_result["scope"],
result=spf_result["result"])
index = "dmarc_aggregate"
if index_suffix:
index = "{0}_{1}".format(index, index_suffix)
index = "{0}-{1}".format(index, index_date)
index_settings = dict(number_of_shards=number_of_shards,
number_of_replicas=number_of_replicas)
create_indexes([index], index_settings)
agg_doc.meta.index = index
try:
agg_doc.save()
except Exception as e:
raise ElasticsearchError(
"Elasticsearch error: {0}".format(e.__str__()))
[docs]def save_forensic_report_to_elasticsearch(forensic_report,
index_suffix=None,
monthly_indexes=False,
number_of_shards=1,
number_of_replicas=0):
"""
Saves a parsed DMARC forensic report to ElasticSearch
Args:
forensic_report (OrderedDict): A parsed forensic report
index_suffix (str): The suffix of the name of the index to save to
monthly_indexes (bool): Use monthly indexes instead of daily
indexes
number_of_shards (int): The number of shards to use in the index
number_of_replicas (int): The number of replicas to use in the
index
Raises:
AlreadySaved
"""
logger.info("Saving forensic report to Elasticsearch")
forensic_report = forensic_report.copy()
sample_date = None
if forensic_report["parsed_sample"]["date"] is not None:
sample_date = forensic_report["parsed_sample"]["date"]
sample_date = human_timestamp_to_datetime(sample_date)
original_headers = forensic_report["parsed_sample"]["headers"]
headers = OrderedDict()
for original_header in original_headers:
headers[original_header.lower()] = original_headers[original_header]
arrival_date_human = forensic_report["arrival_date_utc"]
arrival_date = human_timestamp_to_datetime(arrival_date_human)
if index_suffix is not None:
search = Search(index="dmarc_forensic_{0}*".format(index_suffix))
else:
search = Search(index="dmarc_forensic*")
arrival_query = {"match": {"arrival_date": arrival_date}}
q = Q(arrival_query)
from_ = None
to_ = None
subject = None
if "from" in headers:
from_ = headers["from"]
from_query = {"match_phrase": {"sample.headers.from": from_}}
q = q & Q(from_query)
if "to" in headers:
to_ = headers["to"]
to_query = {"match_phrase": {"sample.headers.to": to_}}
q = q & Q(to_query)
if "subject" in headers:
subject = headers["subject"]
subject_query = {"match_phrase": {"sample.headers.subject": subject}}
q = q & Q(subject_query)
search.query = q
existing = search.execute()
if len(existing) > 0:
raise AlreadySaved("A forensic sample to {0} from {1} "
"with a subject of {2} and arrival date of {3} "
"already exists in "
"Elasticsearch".format(to_,
from_,
subject,
arrival_date_human
))
parsed_sample = forensic_report["parsed_sample"]
sample = _ForensicSampleDoc(
raw=forensic_report["sample"],
headers=headers,
headers_only=forensic_report["sample_headers_only"],
date=sample_date,
subject=forensic_report["parsed_sample"]["subject"],
filename_safe_subject=parsed_sample["filename_safe_subject"],
body=forensic_report["parsed_sample"]["body"]
)
for address in forensic_report["parsed_sample"]["to"]:
sample.add_to(display_name=address["display_name"],
address=address["address"])
for address in forensic_report["parsed_sample"]["reply_to"]:
sample.add_reply_to(display_name=address["display_name"],
address=address["address"])
for address in forensic_report["parsed_sample"]["cc"]:
sample.add_cc(display_name=address["display_name"],
address=address["address"])
for address in forensic_report["parsed_sample"]["bcc"]:
sample.add_bcc(display_name=address["display_name"],
address=address["address"])
for attachment in forensic_report["parsed_sample"]["attachments"]:
sample.add_attachment(filename=attachment["filename"],
content_type=attachment["mail_content_type"],
sha256=attachment["sha256"])
try:
forensic_doc = _ForensicReportDoc(
feedback_type=forensic_report["feedback_type"],
user_agent=forensic_report["user_agent"],
version=forensic_report["version"],
original_mail_from=forensic_report["original_mail_from"],
arrival_date=arrival_date,
domain=forensic_report["reported_domain"],
original_envelope_id=forensic_report["original_envelope_id"],
authentication_results=forensic_report["authentication_results"],
delivery_results=forensic_report["delivery_result"],
source_ip_address=forensic_report["source"]["ip_address"],
source_country=forensic_report["source"]["country"],
source_reverse_dns=forensic_report["source"]["reverse_dns"],
source_base_domain=forensic_report["source"]["base_domain"],
authentication_mechanisms=forensic_report[
"authentication_mechanisms"],
auth_failure=forensic_report["auth_failure"],
dkim_domain=forensic_report["dkim_domain"],
original_rcpt_to=forensic_report["original_rcpt_to"],
sample=sample
)
index = "dmarc_forensic"
if index_suffix:
index = "{0}_{1}".format(index, index_suffix)
if monthly_indexes:
index_date = arrival_date.strftime("%Y-%m")
else:
index_date = arrival_date.strftime("%Y-%m-%d")
index = "{0}-{1}".format(index, index_date)
index_settings = dict(number_of_shards=number_of_shards,
number_of_replicas=number_of_replicas)
create_indexes([index], index_settings)
forensic_doc.meta.index = index
try:
forensic_doc.save()
except Exception as e:
raise ElasticsearchError(
"Elasticsearch error: {0}".format(e.__str__()))
except KeyError as e:
raise InvalidForensicReport(
"Forensic report missing required field: {0}".format(e.__str__()))