def test4learn_from_examples_with_errors_and_smaller_probabilities(self):
"""
In this test case examples with errors are used, but still should be learned.
These tests are using a higher generation_probability and generation_factor, because the data contains errors.
"""
description = 'test4eventCorrelationDetectorTest'
ecd = EventCorrelationDetector(self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True,
generation_probability=0.7, generation_factor=0.99, auto_include_flag=True)
self.analysis_context.register_component(ecd, description)
self.run_ecd_test(ecd, self.errored_data_diff5_low_error_rate[:25000])
ecd = EventCorrelationDetector(self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True,
generation_probability=0.7, generation_factor=0.99, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '2')
self.run_ecd_test(ecd, self.errored_data_diff1_low_error_rate[:25000])
ecd = EventCorrelationDetector(self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True,
generation_probability=0.5, generation_factor=0.95, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '3')
self.run_ecd_test(ecd, self.errored_data_diff5_low_error_rate[:40000])
ecd = EventCorrelationDetector(self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True,
generation_probability=0.5, generation_factor=0.95, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '4')
self.run_ecd_test(ecd, self.errored_data_diff1_low_error_rate[:40000])
def test2learn_from_clear_examples_with_smaller_probabilities(self):
"""
Like in test1 perfect examples are used.
The generation_probability and generation_factor are set to 0.5 in the first case and 0.1 in the second case.
The EventCorrelationDetector should still learn the rules as expected.
"""
description = 'test2eventCorrelationDetectorTest'
ecd = EventCorrelationDetector(self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True,
generation_probability=0.5, generation_factor=0.5, auto_include_flag=True)
self.analysis_context.register_component(ecd, description)
self.run_ecd_test(ecd, self.perfect_data_diff5[:30000])
ecd = EventCorrelationDetector(self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True,
generation_probability=0.5, generation_factor=0.5, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '2')
self.run_ecd_test(ecd, self.perfect_data_diff1[:30000])
ecd = EventCorrelationDetector(self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True,
generation_probability=0.3, generation_factor=0.3, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '3')
self.run_ecd_test(ecd, self.perfect_data_diff5[:100000])
ecd = EventCorrelationDetector(self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True,
generation_probability=0.3, generation_factor=0.3, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '4')
self.run_ecd_test(ecd, self.perfect_data_diff1[:100000])
def test6approximately_learn_implications(self):
"""
In this unittest p0 and alpha are chosen to approximately find sequences in log data.
Therefor not as many iterations are needed to learn the rules.
"""
description = 'test6eventCorrelationDetectorTest'
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, p0=0.7, alpha=0.1, auto_include_flag=True)
self.analysis_context.register_component(ecd, description)
self.run_ecd_test(ecd, self.perfect_data_diff5[:10000])
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, p0=0.7, alpha=0.1, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '2')
self.run_ecd_test(ecd, self.errored_data_diff5[:10000])
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, p0=0.7, alpha=0.1, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '3')
self.run_ecd_test(ecd, self.perfect_data_diff1[:10000])
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, p0=0.7, alpha=0.1, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '4')
self.run_ecd_test(ecd, self.errored_data_diff1[:10000])
def test5learn_safe_assumptions(self):
"""
In this test case p0 and alpha are chosen carefully to only find safe assumptions about the implications in the data.
Therefor more iterations in the training phase are needed.
"""
description = 'test5eventCorrelationDetectorTest'
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, p0=1.0, alpha=0.01, auto_include_flag=True)
self.analysis_context.register_component(ecd, description)
self.run_ecd_test(ecd, self.perfect_data_diff5[:20000])
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, p0=1.0, alpha=0.01, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '2')
self.run_ecd_test(ecd, self.errored_data_diff5_low_error_rate[:40000])
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, p0=1.0, alpha=0.01, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '3')
self.run_ecd_test(ecd, self.perfect_data_diff1[:20000])
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, p0=1.0, alpha=0.01, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '4')
self.run_ecd_test(ecd, self.errored_data_diff1_low_error_rate[:40000])
def test3learn_from_examples_with_errors(self):
"""In this test case examples with errors are used, but still should be learned. The same parameters like in test1 are used."""
description = 'test3eventCorrelationDetectorTest'
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, auto_include_flag=True)
self.analysis_context.register_component(ecd, description)
self.run_ecd_test(ecd, self.errored_data_diff5[:12000])
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '2')
self.run_ecd_test(ecd, self.errored_data_diff1[:12000])
def test1learn_from_clear_examples(self):
"""In this test case perfect examples are used to learn and evaluate rules. The default parameters are used."""
description = 'test1eventCorrelationDetectorTest'
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, auto_include_flag=True)
self.analysis_context.register_component(ecd, description)
self.run_ecd_test(ecd, self.perfect_data_diff5[:12000])
ecd = EventCorrelationDetector(
self.aminer_config, [self.stream_printer_event_handler], check_rules_flag=True, auto_include_flag=True)
self.analysis_context.register_component(ecd, description + '2')
self.run_ecd_test(ecd, self.perfect_data_diff1[:12000])
def test7constraint_list(self):
"""Test the allowlisting of paths."""
description = 'test7eventCorrelationDetectorTest'
ecd = EventCorrelationDetector(self.aminer_config,
[self.stream_printer_event_handler],
check_rules_flag=True,
p0=0.7,
alpha=0.1,
auto_include_flag=True)
self.analysis_context.register_component(ecd, description)
self.assertEqual([], ecd.constraint_list)
match_context_fixed_dme = MatchContext(b' pid=')
fixed_dme = FixedDataModelElement('s1', b' pid=')
match_element_fixed_dme = fixed_dme.get_match_element(
"", match_context_fixed_dme)
# unknown path
ecd.allowlist_event(self.analysis % ecd.__class__.__name__,
match_element_fixed_dme.get_path(), None)
self.assertEqual(['/s1'], ecd.constraint_list)
# known path
ecd.allowlist_event(self.analysis % ecd.__class__.__name__,
match_element_fixed_dme.get_path(), None)
self.assertEqual(['/s1'], ecd.constraint_list)
def build_analysis_pipeline(analysis_context):
"""
Define the function to create pipeline for parsing the log data.
It has also to define an AtomizerFactory to instruct aminer how to process incoming data streams to create log atoms from them.
"""
# Build the parsing model:
service_children_disk_report = [
FixedDataModelElement(
'Space',
b' Current Disk Data is: Filesystem Type Size Used Avail Use%'
),
DelimitedDataModelElement('Data', b'%'),
AnyByteDataModelElement('Rest')
]
service_children_login_details = [
FixedDataModelElement('User', b'User '),
DelimitedDataModelElement('Username', b' '),
FixedWordlistDataModelElement('Status',
[b' logged in', b' logged out']),
OptionalMatchModelElement(
'PastTime',
SequenceModelElement('Time', [
FixedDataModelElement('Blank', b' '),
DecimalIntegerValueModelElement('Minutes'),
FixedDataModelElement('Ago', b' minutes ago.')
]))
]
service_children_cron_job = [
DateTimeModelElement('DTM', b'%Y-%m-%d %H:%M:%S'),
FixedDataModelElement('UNameSpace1', b' '),
DelimitedDataModelElement('UName', b' '),
FixedDataModelElement('UNameSpace2', b' '),
DelimitedDataModelElement('User', b' '),
FixedDataModelElement('Cron', b' cron['),
DecimalIntegerValueModelElement('JobNumber'),
FixedDataModelElement('Details', b']: Job `cron.daily` started.')
]
service_children_random_time = [
FixedDataModelElement('Space', b'Random: '),
DecimalIntegerValueModelElement('Random')
]
service_children_sensors = [
SequenceModelElement('CPUTemp', [
FixedDataModelElement('FixedTemp', b'CPU Temp: '),
DecimalIntegerValueModelElement('Temp'),
FixedDataModelElement('Degrees', b'\xc2\xb0C')
]),
FixedDataModelElement('Space1', b', '),
SequenceModelElement('CPUWorkload', [
FixedDataModelElement('FixedWorkload', b'CPUWorkload: '),
DecimalIntegerValueModelElement('Workload'),
FixedDataModelElement('Percent', b'%')
]),
FixedDataModelElement('Space2', b', '),
DateTimeModelElement('DTM', b'%Y-%m-%d %H:%M:%S')
]
service_children_user_ip_address = [
FixedDataModelElement('User', b'User '),
DelimitedDataModelElement('Username', b' '),
FixedDataModelElement('Action', b' changed IP address to '),
IpAddressDataModelElement('IP')
]
service_children_cron_job_announcement = [
DateTimeModelElement('DTM', b'%Y-%m-%d %H:%M:%S'),
FixedDataModelElement('Space', b' '),
DelimitedDataModelElement('UName', b' '),
FixedDataModelElement('Cron', b' cron['),
DecimalIntegerValueModelElement('JobNumber'),
FixedDataModelElement('Run', b']: Will run job `'),
FixedWordlistDataModelElement(
'CronType',
[b'cron.daily', b'cron.hourly', b'cron.monthly', b'cron.weekly']),
FixedDataModelElement('StartTime', b'\' in 5 min.')
]
service_children_cron_job_execution = [
DateTimeModelElement('DTM', b'%Y-%m-%d %H:%M:%S'),
FixedDataModelElement('Space1', b' '),
DelimitedDataModelElement('UName', b' '),
FixedDataModelElement('Cron', b' cron['),
DecimalIntegerValueModelElement('JobNumber'),
FixedDataModelElement('Job', b']: Job `'),
FixedWordlistDataModelElement(
'CronType',
[b'cron.daily', b'cron.hourly', b'cron.monthly', b'cron.weekly']),
FixedDataModelElement('Started', b'\' started')
]
parsing_model = FirstMatchModelElement('model', [
SequenceModelElement('CronAnnouncement',
service_children_cron_job_announcement),
SequenceModelElement('CronExecution',
service_children_cron_job_execution),
SequenceModelElement('DailyCron', service_children_cron_job),
SequenceModelElement('DiskReport', service_children_disk_report),
SequenceModelElement('LoginDetails', service_children_login_details),
DecimalIntegerValueModelElement('Random'),
SequenceModelElement('RandomTime', service_children_random_time),
SequenceModelElement('Sensors', service_children_sensors),
SequenceModelElement('IPAddresses', service_children_user_ip_address)
])
# Some generic imports.
from aminer.analysis import AtomFilters
# Create all global handler lists here and append the real handlers later on.
# Use this filter to distribute all atoms to the analysis handlers.
atom_filters = AtomFilters.SubhandlerFilter(None)
analysis_context.register_component(atom_filters,
component_name="AtomFilter")
from aminer.analysis.TimestampCorrectionFilters import SimpleMonotonicTimestampAdjust
simple_monotonic_timestamp_adjust = SimpleMonotonicTimestampAdjust(
[atom_filters])
analysis_context.register_component(
simple_monotonic_timestamp_adjust,
component_name="SimpleMonotonicTimestampAdjust")
from aminer.events.StreamPrinterEventHandler import StreamPrinterEventHandler
stream_printer_event_handler = StreamPrinterEventHandler(
analysis_context) # skipcq: BAN-B108
from aminer.events.Utils import VolatileLogarithmicBackoffEventHistory
volatile_logarithmic_backoff_event_history = VolatileLogarithmicBackoffEventHistory(
100)
anomaly_event_handlers = [
stream_printer_event_handler,
volatile_logarithmic_backoff_event_history
]
analysis_context.register_component(
volatile_logarithmic_backoff_event_history,
component_name="VolatileLogarithmicBackoffEventHistory")
# Now define the AtomizerFactory using the model. A simple line based one is usually sufficient.
from aminer.input.SimpleByteStreamLineAtomizerFactory import SimpleByteStreamLineAtomizerFactory
analysis_context.atomizer_factory = SimpleByteStreamLineAtomizerFactory(
parsing_model, [simple_monotonic_timestamp_adjust],
anomaly_event_handlers)
# Just report all unparsed atoms to the event handlers.
from aminer.analysis.UnparsedAtomHandlers import SimpleUnparsedAtomHandler
simple_unparsed_atom_handler = SimpleUnparsedAtomHandler(
anomaly_event_handlers)
atom_filters.add_handler(simple_unparsed_atom_handler,
stop_when_handled_flag=True)
analysis_context.register_component(simple_unparsed_atom_handler,
component_name="UnparsedHandler")
from aminer.analysis.TimestampsUnsortedDetector import TimestampsUnsortedDetector
timestamps_unsorted_detector = TimestampsUnsortedDetector(
analysis_context.aminer_config, anomaly_event_handlers)
atom_filters.add_handler(timestamps_unsorted_detector)
analysis_context.register_component(
timestamps_unsorted_detector,
component_name="TimestampsUnsortedDetector")
from aminer.analysis import Rules
from aminer.analysis.AllowlistViolationDetector import AllowlistViolationDetector
allowlist_rules = [
Rules.OrMatchRule([
Rules.AndMatchRule([
Rules.PathExistsMatchRule(
'/model/LoginDetails/PastTime/Time/Minutes'),
Rules.NegationMatchRule(
Rules.ValueMatchRule('/model/LoginDetails/Username',
b'root'))
]),
Rules.AndMatchRule([
Rules.NegationMatchRule(
Rules.PathExistsMatchRule(
'/model/LoginDetails/PastTime/Time/Minutes')),
Rules.PathExistsMatchRule('/model/LoginDetails')
]),
Rules.NegationMatchRule(
Rules.PathExistsMatchRule('/model/LoginDetails'))
])
]
# This rule list should trigger, when the line does not look like: User root (logged in, logged out)
# or User 'username' (logged in, logged out) x minutes ago.
allowlist_violation_detector = AllowlistViolationDetector(
analysis_context.aminer_config, allowlist_rules,
anomaly_event_handlers)
analysis_context.register_component(allowlist_violation_detector,
component_name="Allowlist")
atom_filters.add_handler(allowlist_violation_detector)
from aminer.analysis.ParserCount import ParserCount
parser_count = ParserCount(analysis_context.aminer_config, None,
anomaly_event_handlers, 10)
analysis_context.register_component(parser_count,
component_name="ParserCount")
atom_filters.add_handler(parser_count)
from aminer.analysis.EventCorrelationDetector import EventCorrelationDetector
ecd = EventCorrelationDetector(analysis_context.aminer_config,
anomaly_event_handlers,
check_rules_flag=True,
hypothesis_max_delta_time=1.0,
auto_include_flag=True)
analysis_context.register_component(
ecd, component_name="EventCorrelationDetector")
atom_filters.add_handler(ecd)
from aminer.analysis.NewMatchPathDetector import NewMatchPathDetector
new_match_path_detector = NewMatchPathDetector(
analysis_context.aminer_config,
anomaly_event_handlers,
auto_include_flag=True)
analysis_context.register_component(new_match_path_detector,
component_name="NewMatchPath")
atom_filters.add_handler(new_match_path_detector)
def tuple_transformation_function(match_value_list):
"""Only allow output of the EnhancedNewMatchPathValueComboDetector after every 10000th element."""
extra_data = enhanced_new_match_path_value_combo_detector.known_values_dict.get(
tuple(match_value_list))
if extra_data is not None:
mod = 10000
if (extra_data[2] + 1) % mod == 0:
enhanced_new_match_path_value_combo_detector.auto_include_flag = False
else:
enhanced_new_match_path_value_combo_detector.auto_include_flag = True
return match_value_list
from aminer.analysis.EnhancedNewMatchPathValueComboDetector import EnhancedNewMatchPathValueComboDetector
enhanced_new_match_path_value_combo_detector = EnhancedNewMatchPathValueComboDetector(
analysis_context.aminer_config,
['/model/DailyCron/UName', '/model/DailyCron/JobNumber'],
anomaly_event_handlers,
auto_include_flag=False,
tuple_transformation_function=tuple_transformation_function)
analysis_context.register_component(
enhanced_new_match_path_value_combo_detector,
component_name="EnhancedNewValueCombo")
atom_filters.add_handler(enhanced_new_match_path_value_combo_detector)
from aminer.analysis.HistogramAnalysis import HistogramAnalysis, LinearNumericBinDefinition, ModuloTimeBinDefinition, \
PathDependentHistogramAnalysis
modulo_time_bin_definition = ModuloTimeBinDefinition(
86400, 3600, 0, 1, 24, True)
linear_numeric_bin_definition = LinearNumericBinDefinition(50, 5, 20, True)
histogram_analysis = HistogramAnalysis(
analysis_context.aminer_config,
[('/model/RandomTime/Random', modulo_time_bin_definition),
('/model/Random', linear_numeric_bin_definition)], 10,
anomaly_event_handlers)
analysis_context.register_component(histogram_analysis,
component_name="HistogramAnalysis")
atom_filters.add_handler(histogram_analysis)
path_dependent_histogram_analysis = PathDependentHistogramAnalysis(
analysis_context.aminer_config, '/model/RandomTime',
modulo_time_bin_definition, 10, anomaly_event_handlers)
analysis_context.register_component(
path_dependent_histogram_analysis,
component_name="PathDependentHistogramAnalysis")
atom_filters.add_handler(path_dependent_histogram_analysis)
from aminer.analysis.MatchValueAverageChangeDetector import MatchValueAverageChangeDetector
match_value_average_change_detector = MatchValueAverageChangeDetector(
analysis_context.aminer_config, anomaly_event_handlers, None,
['/model/Random'], 100, 10)
analysis_context.register_component(
match_value_average_change_detector,
component_name="MatchValueAverageChange")
atom_filters.add_handler(match_value_average_change_detector)
import sys
from aminer.analysis.MatchValueStreamWriter import MatchValueStreamWriter
match_value_stream_writer = MatchValueStreamWriter(sys.stdout, [
'/model/Sensors/CPUTemp', '/model/Sensors/CPUWorkload',
'/model/Sensors/DTM'
], b';', b'')
analysis_context.register_component(
match_value_stream_writer, component_name="MatchValueStreamWriter")
atom_filters.add_handler(match_value_stream_writer)
from aminer.analysis.NewMatchPathValueComboDetector import NewMatchPathValueComboDetector
new_match_path_value_combo_detector = NewMatchPathValueComboDetector(
analysis_context.aminer_config,
['/model/IPAddresses/Username', '/model/IPAddresses/IP'],
anomaly_event_handlers,
auto_include_flag=False)
analysis_context.register_component(
new_match_path_value_combo_detector,
component_name="NewMatchPathValueCombo")
atom_filters.add_handler(new_match_path_value_combo_detector)
from aminer.analysis.NewMatchIdValueComboDetector import NewMatchIdValueComboDetector
new_match_id_value_combo_detector = NewMatchIdValueComboDetector(
analysis_context.aminer_config,
['/model/type/path/name', '/model/type/syscall/syscall'],
anomaly_event_handlers,
id_path_list=['/model/type/path/id', '/model/type/syscall/id'],
min_allowed_time_diff=5,
auto_include_flag=True,
allow_missing_values_flag=True,
output_log_line=True)
analysis_context.register_component(
new_match_id_value_combo_detector,
component_name="NewMatchIdValueComboDetector")
atom_filters.add_handler(new_match_id_value_combo_detector)
from aminer.analysis.NewMatchPathValueDetector import NewMatchPathValueDetector
new_match_path_value_detector = NewMatchPathValueDetector(
analysis_context.aminer_config,
['/model/DailyCron/Job Number', '/model/IPAddresses/Username'],
anomaly_event_handlers,
auto_include_flag=False)
analysis_context.register_component(new_match_path_value_detector,
component_name="NewMatchPathValue")
atom_filters.add_handler(new_match_path_value_detector)
from aminer.analysis.MissingMatchPathValueDetector import MissingMatchPathValueDetector
missing_match_path_value_detector = MissingMatchPathValueDetector(
analysis_context.aminer_config, ['/model/DiskReport/Space'],
anomaly_event_handlers,
auto_include_flag=False,
default_interval=2,
realert_interval=5)
analysis_context.register_component(missing_match_path_value_detector,
component_name="MissingMatch")
atom_filters.add_handler(missing_match_path_value_detector)
from aminer.analysis.TimeCorrelationDetector import TimeCorrelationDetector
time_correlation_detector = TimeCorrelationDetector(
analysis_context.aminer_config,
anomaly_event_handlers,
2,
min_rule_attributes=1,
max_rule_attributes=5,
record_count_before_event=70000,
output_log_line=True)
analysis_context.register_component(
time_correlation_detector, component_name="TimeCorrelationDetector")
atom_filters.add_handler(time_correlation_detector)
from aminer.analysis.TimeCorrelationViolationDetector import TimeCorrelationViolationDetector, CorrelationRule, EventClassSelector
cron_job_announcement = CorrelationRule(
'CronJobAnnouncement',
5,
6,
max_artefacts_a_for_single_b=1,
artefact_match_parameters=[('/model/CronAnnouncement/JobNumber',
'/model/CronExecution/JobNumber')])
a_class_selector = EventClassSelector('Announcement',
[cron_job_announcement], None)
b_class_selector = EventClassSelector('Execution', None,
[cron_job_announcement])
rules = [
Rules.PathExistsMatchRule('/model/CronAnnouncement/Run',
a_class_selector),
Rules.PathExistsMatchRule('/model/CronExecution/Job', b_class_selector)
]
time_correlation_violation_detector = TimeCorrelationViolationDetector(
analysis_context.aminer_config, rules, anomaly_event_handlers)
analysis_context.register_component(
time_correlation_violation_detector,
component_name="TimeCorrelationViolationDetector")
atom_filters.add_handler(time_correlation_violation_detector)
from aminer.events.DefaultMailNotificationEventHandler import DefaultMailNotificationEventHandler
if DefaultMailNotificationEventHandler.CONFIG_KEY_MAIL_TARGET_ADDRESS in analysis_context.aminer_config.config_properties:
mail_notification_handler = DefaultMailNotificationEventHandler(
analysis_context)
analysis_context.register_component(mail_notification_handler,
component_name="MailHandler")
anomaly_event_handlers.append(mail_notification_handler)
def build_analysis_pipeline(analysis_context):
"""
Define the function to create pipeline for parsing the log data.
It has also to define an AtomizerFactory to instruct aminer how to process incoming data streams to create log atoms from them.
"""
date_format_string = b'%Y-%m-%d %H:%M:%S'
cron = b' cron['
# Build the parsing model:
service_children_disk_report = [
FixedDataModelElement('Space', b' Current Disk Data is: Filesystem Type Size Used Avail Use%'),
DelimitedDataModelElement('Data', b'%'), AnyByteDataModelElement('Rest')]
service_children_login_details = [
FixedDataModelElement('User/LoginDetails', b'User '), DelimitedDataModelElement('Username', b' '),
FixedWordlistDataModelElement('Status', [b' logged in', b' logged out']),
OptionalMatchModelElement('PastTime', SequenceModelElement('Time', [
FixedDataModelElement('Blank', b' '), DecimalIntegerValueModelElement('Minutes'),
FixedDataModelElement('Ago', b' minutes ago.')]))]
service_children_cron_job = [
DateTimeModelElement('DTM', date_format_string), FixedDataModelElement('UNameSpace1', b' '),
DelimitedDataModelElement('UName', b' '), FixedDataModelElement('UNameSpace2', b' '), DelimitedDataModelElement('User', b' '),
FixedDataModelElement('Cron', cron), DecimalIntegerValueModelElement('JobNumber'),
FixedDataModelElement('Details', b']: Job `cron.daily` started.')]
service_children_random_time = [FixedDataModelElement('Space', b'Random: '), DecimalIntegerValueModelElement('Random')]
service_children_sensors = [SequenceModelElement('CPUTemp', [
FixedDataModelElement('FixedTemp', b'CPU Temp: '), DecimalIntegerValueModelElement('Temp'),
FixedDataModelElement('Degrees', b'\xc2\xb0C')]), FixedDataModelElement('Space1', b', '), SequenceModelElement('CPUWorkload', [
FixedDataModelElement('FixedWorkload', b'CPU Workload: '), DecimalIntegerValueModelElement('Workload'),
FixedDataModelElement('Percent', b'%')]), FixedDataModelElement('Space2', b', '),
DateTimeModelElement('DTM', date_format_string)]
service_children_user_ip_address = [
FixedDataModelElement('User/UserIPAddress', b'User '), DelimitedDataModelElement('Username', b' '),
FixedDataModelElement('Action', b' changed IP address to '), IpAddressDataModelElement('IP')]
service_children_cron_job_announcement = [
DateTimeModelElement('DTM', date_format_string), FixedDataModelElement('Space', b' '),
DelimitedDataModelElement('UName', b' '), FixedDataModelElement('Cron', cron), DecimalIntegerValueModelElement('JobNumber'),
FixedDataModelElement('Run', b']: Will run job `'),
FixedWordlistDataModelElement('CronType', [b'cron.daily', b'cron.hourly', b'cron.monthly', b'cron.weekly']),
FixedDataModelElement('StartTime', b'\' in 5 min.')]
service_children_cron_job_execution = [
DateTimeModelElement('DTM', date_format_string), FixedDataModelElement('Space1', b' '),
DelimitedDataModelElement('UName', b' '), FixedDataModelElement('Cron', cron), DecimalIntegerValueModelElement('JobNumber'),
FixedDataModelElement('Job', b']: Job `'),
FixedWordlistDataModelElement('CronType', [b'cron.daily', b'cron.hourly', b'cron.monthly', b'cron.weekly']),
FixedDataModelElement('Started', b'\' started')]
service_children_audit = [SequenceModelElement('path', [
FixedDataModelElement('type', b'type=PATH '), FixedDataModelElement('msg_audit', b'msg=audit('),
DelimitedDataModelElement('msg', b':'), FixedDataModelElement('placeholder', b':'), DecimalIntegerValueModelElement('id'),
FixedDataModelElement('item_string', b'): item='), DecimalIntegerValueModelElement('item'),
FixedDataModelElement('name_string', b' name="'), DelimitedDataModelElement('name', b'"'),
FixedDataModelElement('inode_string', b'" inode='), DecimalIntegerValueModelElement('inode'),
FixedDataModelElement('dev_string', b' dev='), DelimitedDataModelElement('dev', b' '),
FixedDataModelElement('mode_string', b' mode='),
DecimalIntegerValueModelElement('mode', value_pad_type=DecimalIntegerValueModelElement.PAD_TYPE_ZERO),
FixedDataModelElement('ouid_string', b' ouid='), DecimalIntegerValueModelElement('ouid'),
FixedDataModelElement('ogid_string', b' ogid='), DecimalIntegerValueModelElement('ogid'),
FixedDataModelElement('rdev_string', b' rdev='), DelimitedDataModelElement('rdev', b' '),
FixedDataModelElement('nametype_string', b' nametype='), FixedWordlistDataModelElement('nametype', [b'NORMAL', b'ERROR'])]),
SequenceModelElement('syscall', [
FixedDataModelElement('type', b'type=SYSCALL '), FixedDataModelElement('msg_audit', b'msg=audit('),
DelimitedDataModelElement('msg', b':'), FixedDataModelElement('placeholder', b':'), DecimalIntegerValueModelElement('id'),
FixedDataModelElement('arch_string', b'): arch='), DelimitedDataModelElement('arch', b' '),
FixedDataModelElement('syscall_string', b' syscall='), DecimalIntegerValueModelElement('syscall'),
FixedDataModelElement('success_string', b' success='), FixedWordlistDataModelElement('success', [b'yes', b'no']),
FixedDataModelElement('exit_string', b' exit='), DecimalIntegerValueModelElement('exit'),
AnyByteDataModelElement('remainding_data')])]
service_children_parsing_model_element = [
DateTimeModelElement('DateTimeModelElement', b'Current DateTime: %d.%m.%Y %H:%M:%S'),
DecimalFloatValueModelElement('DecimalFloatValueModelElement', value_sign_type='optional'),
DecimalIntegerValueModelElement('DecimalIntegerValueModelElement', value_sign_type='optional', value_pad_type='blank'),
SequenceModelElement('se', [
DelimitedDataModelElement('DelimitedDataModelElement', b';'), FixedDataModelElement('FixedDataModelElement', b';')])]
# ElementValueBranchModelElement
fixed_data_me1 = FixedDataModelElement("fixed1", b'match ')
fixed_data_me2 = FixedDataModelElement("fixed2", b'fixed String')
fixed_wordlist_data_model_element = FixedWordlistDataModelElement("wordlist", [b'data: ', b'string: '])
decimal_integer_value_model_element = DecimalIntegerValueModelElement("decimal")
service_children_parsing_model_element.append(
ElementValueBranchModelElement('ElementValueBranchModelElement', FirstMatchModelElement("first", [
SequenceModelElement("seq1", [fixed_data_me1, fixed_wordlist_data_model_element]),
SequenceModelElement("seq2", [fixed_data_me1, fixed_wordlist_data_model_element, fixed_data_me2])]), "wordlist",
{0: decimal_integer_value_model_element, 1: fixed_data_me2}))
service_children_parsing_model_element.append(HexStringModelElement('HexStringModelElement'))
service_children_parsing_model_element.append(SequenceModelElement('se2', [
FixedDataModelElement('FixedDataModelElement', b'Gateway IP-Address: '), IpAddressDataModelElement('IpAddressDataModelElement')]))
import locale
loc = locale.getlocale()
if loc == (None, None):
loc = ('en_US', 'utf8')
service_children_parsing_model_element.append(
MultiLocaleDateTimeModelElement('MultiLocaleDateTimeModelElement', [(b'%b %d %Y', None, '%s.%s' % loc)]))
service_children_parsing_model_element.append(
RepeatedElementDataModelElement('RepeatedElementDataModelElement', SequenceModelElement('SequenceModelElement', [
FixedDataModelElement('FixedDataModelElement', b'[drawn number]: '),
DecimalIntegerValueModelElement('DecimalIntegerValueModelElement')]), 1))
service_children_parsing_model_element.append(VariableByteDataModelElement('VariableByteDataModelElement', b'-@#'))
service_children_parsing_model_element.append(SequenceModelElement('se', [
WhiteSpaceLimitedDataModelElement('WhiteSpaceLimitedDataModelElement'), FixedDataModelElement('fixed', b' ')]))
# The Base64StringModelElement must be just before the AnyByteDataModelElement to avoid unexpected Matches.
service_children_parsing_model_element.append(Base64StringModelElement('Base64StringModelElement'))
# The OptionalMatchModelElement must be paired with a FirstMatchModelElement because it accepts all data and thus no data gets
# to the AnyByteDataModelElement. The AnyByteDataModelElement must be last, because all bytes are accepted.
service_children_parsing_model_element.append(
OptionalMatchModelElement('/', FirstMatchModelElement('FirstMatchModelElement//optional', [
FixedDataModelElement('FixedDataModelElement', b'The-searched-element-was-found!'), SequenceModelElement('se', [
FixedDataModelElement('FixedDME', b'Any:'), AnyByteDataModelElement('AnyByteDataModelElement')])])))
alphabet = b'ghijkl'
service_children_ecd = []
for _, char in enumerate(alphabet):
char = bytes([char])
service_children_ecd.append(FixedDataModelElement(char.decode(), char))
parsing_model = FirstMatchModelElement('model', [
SequenceModelElement('CronAnnouncement', service_children_cron_job_announcement),
SequenceModelElement('CronExecution', service_children_cron_job_execution),
SequenceModelElement('DailyCron', service_children_cron_job), SequenceModelElement('DiskReport', service_children_disk_report),
SequenceModelElement('LoginDetails', service_children_login_details), DecimalIntegerValueModelElement('Random'),
SequenceModelElement('RandomTime', service_children_random_time), SequenceModelElement('Sensors', service_children_sensors),
SequenceModelElement('IPAddresses', service_children_user_ip_address), FirstMatchModelElement('type', service_children_audit),
FirstMatchModelElement('ECD', service_children_ecd), FirstMatchModelElement('ParsingME', service_children_parsing_model_element)])
# Some generic imports.
from aminer.analysis import AtomFilters
# Create all global handler lists here and append the real handlers later on.
# Use this filter to distribute all atoms to the analysis handlers.
atom_filter = AtomFilters.SubhandlerFilter(None)
from aminer.analysis.TimestampCorrectionFilters import SimpleMonotonicTimestampAdjust
simple_monotonic_timestamp_adjust = SimpleMonotonicTimestampAdjust([atom_filter])
analysis_context.register_component(simple_monotonic_timestamp_adjust, component_name="SimpleMonotonicTimestampAdjust")
from aminer.events.StreamPrinterEventHandler import StreamPrinterEventHandler
from aminer.events.JsonConverterHandler import JsonConverterHandler
stream_printer_event_handler = StreamPrinterEventHandler(analysis_context)
json_converter_handler = JsonConverterHandler([stream_printer_event_handler], analysis_context)
anomaly_event_handlers = [json_converter_handler]
# Now define the AtomizerFactory using the model. A simple line based one is usually sufficient.
from aminer.input.SimpleByteStreamLineAtomizerFactory import SimpleByteStreamLineAtomizerFactory
analysis_context.atomizer_factory = SimpleByteStreamLineAtomizerFactory(parsing_model, [simple_monotonic_timestamp_adjust],
anomaly_event_handlers)
# Just report all unparsed atoms to the event handlers.
from aminer.analysis.UnparsedAtomHandlers import SimpleUnparsedAtomHandler, VerboseUnparsedAtomHandler
simple_unparsed_atom_handler = SimpleUnparsedAtomHandler(anomaly_event_handlers)
atom_filter.add_handler(simple_unparsed_atom_handler, stop_when_handled_flag=False)
analysis_context.register_component(simple_unparsed_atom_handler, component_name="SimpleUnparsedHandler")
verbose_unparsed_atom_handler = VerboseUnparsedAtomHandler(anomaly_event_handlers, parsing_model)
atom_filter.add_handler(verbose_unparsed_atom_handler, stop_when_handled_flag=True)
analysis_context.register_component(verbose_unparsed_atom_handler, component_name="VerboseUnparsedHandler")
from aminer.analysis.TimestampsUnsortedDetector import TimestampsUnsortedDetector
timestamps_unsorted_detector = TimestampsUnsortedDetector(analysis_context.aminer_config, anomaly_event_handlers)
atom_filter.add_handler(timestamps_unsorted_detector)
analysis_context.register_component(timestamps_unsorted_detector, component_name="TimestampsUnsortedDetector")
from aminer.analysis import Rules
from aminer.analysis.AllowlistViolationDetector import AllowlistViolationDetector
allowlist_rules = [
Rules.OrMatchRule([
Rules.AndMatchRule([
Rules.PathExistsMatchRule('/model/LoginDetails/PastTime/Time/Minutes'),
Rules.NegationMatchRule(Rules.ValueMatchRule('/model/LoginDetails/Username', b'root')),
Rules.DebugMatchRule(debug_match_result=True)]),
Rules.AndMatchRule([
Rules.NegationMatchRule(Rules.PathExistsMatchRule('/model/LoginDetails/PastTime/Time/Minutes')),
Rules.PathExistsMatchRule('/model/LoginDetails'),
Rules.DebugMatchRule(debug_match_result=True)]),
Rules.NegationMatchRule(Rules.PathExistsMatchRule('/model/LoginDetails'))])]
# This rule list should trigger, when the line does not look like: User root (logged in, logged out)
# or User 'username' (logged in, logged out) x minutes ago.
allowlist_violation_detector = AllowlistViolationDetector(analysis_context.aminer_config, allowlist_rules, anomaly_event_handlers,
output_log_line=True)
analysis_context.register_component(allowlist_violation_detector, component_name="Allowlist")
atom_filter.add_handler(allowlist_violation_detector)
from aminer.analysis.ParserCount import ParserCount
parser_count = ParserCount(analysis_context.aminer_config, None, anomaly_event_handlers, 10)
analysis_context.register_component(parser_count, component_name="ParserCount")
atom_filter.add_handler(parser_count)
from aminer.analysis.EventTypeDetector import EventTypeDetector
etd = EventTypeDetector(analysis_context.aminer_config, anomaly_event_handlers)
analysis_context.register_component(etd, component_name="EventTypeDetector")
atom_filter.add_handler(etd)
from aminer.analysis.VariableTypeDetector import VariableTypeDetector
vtd = VariableTypeDetector(analysis_context.aminer_config, anomaly_event_handlers, etd, silence_output_except_indicator=False,
output_log_line=False, ignore_list=["/model/RandomTime"])
analysis_context.register_component(vtd, component_name="VariableTypeDetector")
atom_filter.add_handler(vtd)
from aminer.analysis.VariableCorrelationDetector import VariableCorrelationDetector
vtd = VariableCorrelationDetector(analysis_context.aminer_config, anomaly_event_handlers, etd, disc_div_thres=0.5,
ignore_list=["/model/RandomTime"])
analysis_context.register_component(vtd, component_name="VariableCorrelationDetector")
atom_filter.add_handler(vtd)
from aminer.analysis.EventCorrelationDetector import EventCorrelationDetector
ecd = EventCorrelationDetector(analysis_context.aminer_config, anomaly_event_handlers, check_rules_flag=True,
hypothesis_max_delta_time=1.0)
analysis_context.register_component(ecd, component_name="EventCorrelationDetector")
atom_filter.add_handler(ecd)
from aminer.analysis.EventFrequencyDetector import EventFrequencyDetector
efd = EventFrequencyDetector(analysis_context.aminer_config, anomaly_event_handlers, window_size=0.1)
analysis_context.register_component(efd, component_name="EventFrequencyDetector")
atom_filter.add_handler(efd)
from aminer.analysis.EventSequenceDetector import EventSequenceDetector
esd = EventSequenceDetector(analysis_context.aminer_config, anomaly_event_handlers, ['/model/ParsingME'], ignore_list=[
'/model/ECD/g', '/model/ECD/h', '/model/ECD/i', '/model/ECD/j', '/model/ECD/k', '/model/ECD/l', '/model/Random',
'/model/RandomTime', '/model/DailyCron'])
analysis_context.register_component(esd, component_name="EventSequenceDetector")
atom_filter.add_handler(esd)
from aminer.analysis.MatchFilter import MatchFilter
match_filter = MatchFilter(analysis_context.aminer_config, ['/model/Random'], anomaly_event_handlers, target_value_list=[
1, 10, 100], output_log_line=True)
analysis_context.register_component(match_filter, component_name="MatchFilter")
atom_filter.add_handler(match_filter)
from aminer.analysis.NewMatchPathDetector import NewMatchPathDetector
new_match_path_detector = NewMatchPathDetector(analysis_context.aminer_config, anomaly_event_handlers, auto_include_flag=True,
output_log_line=True)
analysis_context.register_component(new_match_path_detector, component_name="NewMatchPath")
atom_filter.add_handler(new_match_path_detector)
def tuple_transformation_function(match_value_list):
"""Only allow output of the EnhancedNewMatchPathValueComboDetector after every 10th element."""
extra_data = enhanced_new_match_path_value_combo_detector.known_values_dict.get(tuple(match_value_list))
if extra_data is not None:
mod = 10
if (extra_data[2] + 1) % mod == 0:
enhanced_new_match_path_value_combo_detector.auto_include_flag = False
else:
enhanced_new_match_path_value_combo_detector.auto_include_flag = True
return match_value_list
from aminer.analysis.EnhancedNewMatchPathValueComboDetector import EnhancedNewMatchPathValueComboDetector
enhanced_new_match_path_value_combo_detector = EnhancedNewMatchPathValueComboDetector(analysis_context.aminer_config, [
'/model/DailyCron/UName', '/model/DailyCron/JobNumber'], anomaly_event_handlers, auto_include_flag=True,
tuple_transformation_function=tuple_transformation_function, output_log_line=True)
analysis_context.register_component(enhanced_new_match_path_value_combo_detector, component_name="EnhancedNewValueCombo")
atom_filter.add_handler(enhanced_new_match_path_value_combo_detector)
import re
ip_match_action = Rules.EventGenerationMatchAction(
"Analysis.Rules.IPv4InRFC1918MatchRule", "Private IP address occurred!", anomaly_event_handlers)
vdmt = Rules.ValueDependentModuloTimeMatchRule(None, 3, ["/model/ECD/j", "/model/ECD/k", "/model/ECD/l"], {b"e": [0, 2.95]}, [0, 3])
mt = Rules.ModuloTimeMatchRule(None, 3, 0, 3, None)
time_allowlist_rules = [
Rules.AndMatchRule([
Rules.ParallelMatchRule([
Rules.ValueDependentDelegatedMatchRule([
'/model/ECD/g', '/model/ECD/h', '/model/ECD/i', '/model/ECD/j', '/model/ECD/k', '/model/ECD/l'], {
(b"a",): mt, (b"b",): mt, (b"c",): mt, (b"d",): vdmt, (b"e",): vdmt, (b"f",): vdmt, None: mt}, mt),
Rules.IPv4InRFC1918MatchRule("/model/ParsingME/se2/IpAddressDataModelElement", ip_match_action),
Rules.DebugHistoryMatchRule(debug_match_result=True)
]),
# IP addresses 8.8.8.8, 8.8.4.4 and 10.0.0.0 - 10.255.255.255 are not allowed
Rules.NegationMatchRule(Rules.ValueListMatchRule("/model/ParsingME/se2/IpAddressDataModelElement", [134744072, 134743044])),
Rules.NegationMatchRule(Rules.ValueRangeMatchRule("/model/ParsingME/se2/IpAddressDataModelElement", 167772160, 184549375)),
Rules.NegationMatchRule(Rules.StringRegexMatchRule("/model/type/syscall/success", re.compile(b"^no$")))
])
]
time_allowlist_violation_detector = AllowlistViolationDetector(
analysis_context.aminer_config, time_allowlist_rules, anomaly_event_handlers, output_log_line=True)
analysis_context.register_component(time_allowlist_violation_detector, component_name="TimeAllowlist")
atom_filter.add_handler(time_allowlist_violation_detector)
from aminer.analysis.HistogramAnalysis import HistogramAnalysis, LinearNumericBinDefinition, ModuloTimeBinDefinition, \
PathDependentHistogramAnalysis
modulo_time_bin_definition = ModuloTimeBinDefinition(86400, 3600, 0, 1, 24, True)
linear_numeric_bin_definition = LinearNumericBinDefinition(50, 5, 20, True)
histogram_analysis = HistogramAnalysis(analysis_context.aminer_config, [
('/model/RandomTime/Random', modulo_time_bin_definition), ('/model/Random', linear_numeric_bin_definition)], 10,
anomaly_event_handlers, output_log_line=True)
analysis_context.register_component(histogram_analysis, component_name="HistogramAnalysis")
atom_filter.add_handler(histogram_analysis)
path_dependent_histogram_analysis = PathDependentHistogramAnalysis(
analysis_context.aminer_config, '/model/RandomTime', modulo_time_bin_definition, 10, anomaly_event_handlers, output_log_line=True)
analysis_context.register_component(path_dependent_histogram_analysis, component_name="PathDependentHistogramAnalysis")
atom_filter.add_handler(path_dependent_histogram_analysis)
from aminer.analysis.MatchValueAverageChangeDetector import MatchValueAverageChangeDetector
match_value_average_change_detector = MatchValueAverageChangeDetector(analysis_context.aminer_config, anomaly_event_handlers, None, [
'/model/Random'], 100, 10, output_log_line=True)
analysis_context.register_component(match_value_average_change_detector, component_name="MatchValueAverageChange")
atom_filter.add_handler(match_value_average_change_detector)
import sys
from aminer.analysis.MatchValueStreamWriter import MatchValueStreamWriter
match_value_stream_writer = MatchValueStreamWriter(
sys.stdout, ['/model/Sensors/CPUTemp', '/model/Sensors/CPUWorkload', '/model/Sensors/DTM'], b';', b'')
analysis_context.register_component(match_value_stream_writer, component_name="MatchValueStreamWriter")
atom_filter.add_handler(match_value_stream_writer)
from aminer.analysis.NewMatchPathValueComboDetector import NewMatchPathValueComboDetector
new_match_path_value_combo_detector = NewMatchPathValueComboDetector(
analysis_context.aminer_config, ['/model/IPAddresses/Username', '/model/IPAddresses/IP'],
anomaly_event_handlers, output_log_line=True)
analysis_context.register_component(new_match_path_value_combo_detector, component_name="NewMatchPathValueCombo")
atom_filter.add_handler(new_match_path_value_combo_detector)
from aminer.analysis.NewMatchIdValueComboDetector import NewMatchIdValueComboDetector
new_match_id_value_combo_detector = NewMatchIdValueComboDetector(analysis_context.aminer_config, [
'/model/type/path/name', '/model/type/syscall/syscall'], anomaly_event_handlers, id_path_list=[
'/model/type/path/id', '/model/type/syscall/id'], min_allowed_time_diff=5, auto_include_flag=True, allow_missing_values_flag=True,
output_log_line=True)
analysis_context.register_component(new_match_id_value_combo_detector, component_name="NewMatchIdValueComboDetector")
atom_filter.add_handler(new_match_id_value_combo_detector)
from aminer.analysis.NewMatchPathValueDetector import NewMatchPathValueDetector
new_match_path_value_detector = NewMatchPathValueDetector(analysis_context.aminer_config, [
'/model/DailyCron/JobNumber', '/model/IPAddresses/Username'], anomaly_event_handlers, auto_include_flag=True, output_log_line=True)
analysis_context.register_component(new_match_path_value_detector, component_name="NewMatchPathValue")
atom_filter.add_handler(new_match_path_value_detector)
from aminer.analysis.MissingMatchPathValueDetector import MissingMatchPathValueDetector
missing_match_path_value_detector = MissingMatchPathValueDetector(
analysis_context.aminer_config, ['/model/DiskReport/Space'], anomaly_event_handlers, auto_include_flag=True, default_interval=2,
realert_interval=5, output_log_line=True)
analysis_context.register_component(missing_match_path_value_detector, component_name="MissingMatch")
atom_filter.add_handler(missing_match_path_value_detector)
from aminer.analysis.TimeCorrelationDetector import TimeCorrelationDetector
time_correlation_detector = TimeCorrelationDetector(
analysis_context.aminer_config, anomaly_event_handlers, 2, min_rule_attributes=1, max_rule_attributes=5,
record_count_before_event=10000, output_log_line=True)
analysis_context.register_component(time_correlation_detector, component_name="TimeCorrelationDetector")
atom_filter.add_handler(time_correlation_detector)
from aminer.analysis.TimeCorrelationViolationDetector import TimeCorrelationViolationDetector, CorrelationRule, EventClassSelector
cron_job_announcement = CorrelationRule('CronJobAnnouncement', 5, 6, max_artefacts_a_for_single_b=1, artefact_match_parameters=[
('/model/CronAnnouncement/JobNumber', '/model/CronExecution/JobNumber')])
a_class_selector = EventClassSelector('Announcement', [cron_job_announcement], None)
b_class_selector = EventClassSelector('Execution', None, [cron_job_announcement])
rules = [Rules.PathExistsMatchRule('/model/CronAnnouncement/Run', a_class_selector),
Rules.PathExistsMatchRule('/model/CronExecution/Job', b_class_selector)]
time_correlation_violation_detector = TimeCorrelationViolationDetector(analysis_context.aminer_config, rules, anomaly_event_handlers,
output_log_line=True)
analysis_context.register_component(time_correlation_violation_detector, component_name="TimeCorrelationViolationDetector")
atom_filter.add_handler(time_correlation_violation_detector)