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Pattern Recognition with Event Sourcing

1. Definition and purpose

An approach to analyzing event sequences is pattern recognition. By analyzing sequences

of events, patterns and trends can be identified. Understanding the underlying behavior

or processes can be used to predict future events or detect anomalies. Pattern


recognition is a process of identifying regularities or patterns in data and making sense of

them. It involves analyzing and extracting meaningful information from datasets to identify

similarities, relationships and structures within the data. The goal is to uncover hidden

patterns and use them to classify or predict future data instances.

2. Steps in pattern recognition

An event-sourcing application monitors specific entities and their change of state. Pattern

recognition is applied in several ways, including anomaly detection, trend analysis,

sequential pattern mining, predictive modeling, root cause analysis and performance

optimization.

A quick recap can be useful for any of the involved pattern recognition methods.

1. Data preprocessing

The data is cleaned, normalized and transformed into a suitable format for analysis.

This step may involve removing noise, handling missing values or reducing the

dimensionality of the data.

2. Feature extraction

Relevant features or attributes are extracted from the data. This step involves

selecting the most informative aspects of the data that capture the underlying

patterns. Feature extraction is performed using mathematical techniques, statistical

methods or domain-specific knowledge.

3. Pattern representation

The extracted features represent the patterns in a suitable format. This

representation can be numerical, symbolic or graphical, depending on the nature of

the data and the problem at hand.

4. Pattern matching or classification

The represented patterns are compared or matched against known patterns or

models. The application of algorithms or techniques can identify similarities or

dissimilarities between patterns. Based on their similarity to known patterns,

classification algorithms assign new data instances to predefined classes or

categories.

5. Evaluation and validation

The performance of the pattern recognition system is evaluated by comparing the

predicted patterns or classifications with ground truth or expert-labeled data. Various

metrics, such as accuracy, precision, recall or F1 score, can measure the

effectiveness.

3. Mathematical Techniques and Algorithms

High-level mathematical and statistical knowledge is required to perform correct software

coding in pattern recognition.

Statistical methods (probability theory, statistical inference and hypothesis testing) model

and analyze data, estimate parameters and make decisions. Starting from those methods,

machine learning algorithms – decision trees, support vector machines (SVM), neural

networks and k-nearest neighbors (KNN) – form the foundation of pattern recognition

systems required to learn patterns and make predictions based on training data.


A crucial step in pattern recognition is feature extraction, where relevant features or

attributes are extracted from raw data: dimensionality reduction and feature selection

methods are employed to transform and select the most informative features for pattern

analysis.

Clustering algorithms – k-means clustering, hierarchical clustering and density-based

clustering – identify groups or clusters within data based on similarity or distance measures.

In pattern recognition, especially for sequential data analysis, hidden Markov models

(HMM) are used to model and predict sequences of observed events, while bayesian

inference – combining prior knowledge with observed data – estimates unknown

parameters or makes predictions through its networks and classifiers.

4. Applications to event sourcing

An event-sourcing application monitors specific entities and their change of state. Pattern

recognition is applied in several ways, including anomaly detection, trend analysis,

sequential pattern mining, predictive modeling, root cause analysis and performance

optimization. A quick recap can be useful for any of the involved pattern recognition

methods.

Anomaly detection

By analyzing the sequence of events associated with each entity, we can determine patterns

for a normal behavior. Any deviation from these patterns can indicate anomalies or

unexpected changes in the entity's state. Pattern recognition techniques, such as statistical

methods or machine learning algorithms, can detect and flag such anomalies, enabling

proactive actions or notifications.

Trend analysis

We can identify patterns by analyzing the sequence of events over time for each entity.

Trend analysis helps understanding long-term behavior: patterns indicate changes or

tendencies in the entity's state. This information can be valuable for making data-driven

decisions, forecasting future states or identifying emerging patterns.

Sequential pattern mining

Sequential pattern mining algorithms discover recurring sequences of events associated with

specific entities. This analysis can reveal common patterns or state sequences of

changes occurring across different entities. Identifying such patterns can provide insights

into common workflows, dependencies or common behaviors across entities.

Predictive modeling

Predictive models can forecast future states or behavior of entities by leveraging historical

event sequences and their associated state changes. These models use various machine-

learning techniques to capture patterns and dependencies in the event sequence data.

Predictive modeling enables proactive decision-making and intervention based on

anticipated future states.

Root cause analysis

When an entity undergoes unexpected state changes, pattern recognition can trace back

and identify the root cause by analyzing the event sequence leading up to the change.


Identifying patterns or specific event sequences frequently preceding such state changes, it

becomes possible to pinpoint the underlying factors or triggers causing the changes.

Performance optimization

Pattern recognition can analyze the event sequences and identify opportunities for

optimizing the application performance. By identifying patterns of high resource

utilization, frequent state changes or other performance-related patterns, improvements can

enhance the efficiency and scalability of the application.

5. Event chains

In an event-sourcing paradigm, pattern recognition is performed by analyzing the sequence

of events within event chains. Here is the approach we will adopt: starting with the event

sequence, we will extract the relevant features needed to identify meaningful patterns. The

significance of the discovered patterns will finally be the starting point for anomaly

detection, predictive maintenance or process optimization.

In particular, with pattern identification we will use such tools as statistical analysis,

sequence mining and machine learning.

Statistical analysis computes statistical measures such as event frequency, event duration,

or time intervals between events to identify patterns or anomalies in the event chains.

With sequence mining, we use sequence mining algorithms (e.g., sequential pattern mining

or frequent episode discovery) to identify recurring patterns or sequential relationships within

the event chains.

The machine learning phase allows us to train machine learning models on the event

chains to recognize and classify patterns based on the extracted features: this could involve

supervised learning algorithms for pattern classification or unsupervised learning algorithms

for discovering hidden patterns.

1. Event sequence extraction: Retrieve the event chains associated with the entities

or processes of interest from the event sourcing system. An event chain represents

the chronological sequence of events that have occurred for a specific entity or

process.

2. Feature extraction: Extract relevant features or attributes from the event chains.

These features could include event types, timestamps, event parameters or any

other pertinent information that may be useful for pattern recognition.

3. Pattern identification: Apply pattern recognition techniques to the event chains to

identify meaningful patterns. This can involve various methods such as statistical

analysis, sequence mining algorithms or machine learning approaches.

4. Pattern interpretation: Interpret the identified patterns to gain insights or take

appropriate actions. This could involve determining the significance of a pattern,

understanding its implications or using it for decision-making, such as anomaly

detection, predictive maintenance or process optimization.

It is important to note that the specific techniques and algorithms used for pattern recognition

in event chains may vary depending on the nature of the data, the complexity of the patterns

sought, and the objectives of the analysis. Domain expertise and knowledge of the event


sourcing system can also play a crucial role in understanding and interpreting the discovered

patterns.

6. Applications to cybersecurity

Here is how pattern recognition can be applied to cybersecurity: using event sourcing of

network events on Google Cloud can provide valuable insights.

Data collection, event preprocessing, feature extraction and anomaly detection are the

chosen steps.

Let’s see how these steps perform in Python and Big Query SQL code.

1. Data collection

Capture and store network events (e.g., network traffic logs, firewall logs, IDS/IPS alerts)

using event sourcing within Google Cloud's logging and event streaming services (such as

Cloud Logging and Pub/Sub). This ensures the collection of a comprehensive and

timestamped record of network events.

from google.cloud import logging

from google.cloud import pubsub_v1

# Initialize the Cloud Logging and Pub/Sub clients

logging_client = logging.Client()

publisher = pubsub_v1.PublisherClient()

# Define the log name for network events

log_name = 'network-events'

# Function to capture and store network events

def capture_and_store_network_events(event_data):

# Create a Cloud Logging logger

logger = logging_client.logger(log_name)

# Publish network events to Pub/Sub topic for event streaming

topic_path = publisher.topic_path(project_id, topic_id)

for event in event_data:

# Log the network event to Cloud Logging

logger.log_struct(event)

# Publish the network event to Pub/Sub topic

message_bytes = str(event).encode('utf-8')

future = publisher.publish(topic_path, data=message_bytes)

# Wait for the publish operation to complete

future.result()

print('Network events captured and stored successfully.')


# Usage example

event_data = [...] # Network event data to be captured and stored

capture_and_store_network_events(event_data)

2. Event preprocessing

Clean and preprocess the network event data to remove noise, normalize formats and enrich

the events with additional context if necessary. This step involves extracting relevant

information from raw event data, such as source/destination IP addresses, protocols,

timestamps and event types.

import re


# Function to clean and preprocess network event data

def preprocess_network_events(event_data):

cleaned_events = []

# Iterate over each event in the data

for event in event_data:

# Remove noise and irrelevant fields

# Example: Remove fields containing sensitive information or

unnecessary metadata

del event['sensitive_field']

del event['metadata']

# Normalize formats

# Example: Convert timestamps to a standardized format

event['timestamp'] = normalize_timestamp(event['timestamp'])

# Enrich events with additional context if necessary

# Example: Extract additional information from IP addresses

event['source_location'] = geolocate_ip(event['source_ip'])

event['destination_location'] =

geolocate_ip(event['destination_ip'])

# Add the cleaned event to the new list

cleaned_events.append(event)

return cleaned_events

# Function to normalize timestamp format

def normalize_timestamp(timestamp):

# Normalize timestamp format to YYYY-MM-DD HH:MM:SS

# Example: Convert "2022-06-01T15:30:45Z" to "2022-06-01 15:30:45"

cleaned_timestamp = re.sub(r'T|Z', ' ', timestamp)

return cleaned_timestamp


# Function to geolocate IP addresses

def geolocate_ip(ip_address):

# Perform IP geolocation lookup

# Example: Use a geolocation API or database to retrieve location

information based on IP address

location = lookup_ip_geolocation(ip_address)

return location

# Usage example

event_data = [...] # Network event data from Google Cloud

cleaned_data = preprocess_network_events(event_data)

3. Feature extraction

Extract meaningful features from the preprocessed network event data. These features

could include event frequencies, event types, source/destination IP patterns or any other

relevant attributes that can capture the characteristics of network behavior.

from google.cloud import bigquery

# Function to extract meaningful features from preprocessed network

event data using BigQuery

def extract_features_from_bigquery(project_id, dataset_id, table_id):

# Create BigQuery client

client = bigquery.Client(project=project_id)

# SQL query to extract features from the preprocessed network event

data

query = """

SELECT

source_ip,

destination_ip,

EXTRACT(DAYOFWEEK FROM timestamp) AS day_of_week,

EXTRACT(HOUR FROM timestamp) AS hour_of_day,

COUNT(*) AS event_count

FROM

`{project}.{dataset}.{table}`

GROUP BY

source_ip,

destination_ip,

day_of_week,

hour_of_day

""".format(project=project_id, dataset=dataset_id, table=table_id)

# Execute the query and retrieve the results

query_job = client.query(query)


results = query_job.result()

# Process the results and extract the features

features = []

for row in results:

feature = {

'source_ip': row['source_ip'],

'destination_ip': row['destination_ip'],

'day_of_week': row['day_of_week'],

'hour_of_day': row['hour_of_day'],

'event_count': row['event_count'],

}

features.append(feature)

return features

# Usage example

project_id = 'your-project-id'

dataset_id = 'your-dataset-id'

table_id = 'your-table-id'

extracted_features = extract_features_from_bigquery(project_id,

dataset_id, table_id)

4. Anomaly detection

Apply pattern recognition techniques to identify anomalous network behaviors or potential

security threats. It can involve:

● Statistical analysis: compute statistical measures (e.g., mean, standard deviation)

of event frequencies or other network behavior metrics. Identify deviations from

expected patterns, such as sudden spikes or drops in event frequencies, which may

indicate potential security incidents.

● Machine learning: train machine learning models (e.g., anomaly detection

algorithms, clustering algorithms) using historical network event data to learn normal

network behavior. Use these models to detect deviations from the patterns and flag

potential anomalies or malicious activities.

WITH event_data AS (

-- Replace with your own query to retrieve the preprocessed network

event data from BigQuery

SELECT

source_ip,

destination_ip,

EXTRACT(DAYOFWEEK FROM timestamp) AS day_of_week,

EXTRACT(HOUR FROM timestamp) AS hour_of_day,

COUNT(*) AS event_count

FROM

`your-project.your-dataset.your-table`


GROUP BY

source_ip,

destination_ip,

day_of_week,

hour_of_day

),

-- Calculate statistical measures for event count

event_stats AS (

SELECT

source_ip,

destination_ip,

AVG(event_count) AS avg_event_count,

STDDEV(event_count) AS stddev_event_count

FROM

event_data

GROUP BY

source_ip,

destination_ip

),

-- Identify anomalies based on z-scores

anomalies AS (

SELECT

event_data.*,

(event_data.event_count - event_stats.avg_event_count) /

event_stats.stddev_event_count AS z_score

FROM

event_data

JOIN

event_stats

USING

(source_ip, destination_ip)

WHERE

event_data.event_count > 0 -- Exclude zero counts, if applicable

)

-- Query to select anomalous events

SELECT

*

FROM

anomalies

WHERE

z_score > 2 -- Adjust the threshold based on your data and desired

sensitivity


Without writing specific code, we can plot more tasks such as threat intelligence integration,

real-time alerting and response, forensic analysis and pattern correlation (and predictive

analysis).

5. Threat intelligence integration

Integrate external threat intelligence feeds or databases into the pattern recognition system.

Correlate network event patterns with known indicators of compromise (IoCs) or threat

intelligence data, to identify potential security threats or associated patterns with known

malicious activities.

6. Real-time alerting and response

Implement real-time alerting mechanisms to notify security teams or administrators when

potential threats or anomalies are detected. Alerts can be sent via email, instant messaging,

or integrated with incident response platforms.

7. Forensic analysis

Leverage the event sourcing approach to reconstruct the event sequence leading to a

security incident or breach. Use the stored event data to perform detailed forensic analysis,

investigate the root cause and understand the full scope of the incident.

8. Pattern correlation and predictive analysis

Analyze patterns across multiple network events to identify correlations or patterns indicative

of advanced persistent threats (APTs) or sophisticated attack techniques. Apply predictive

analytics to potential security threats based on identified patterns and historical trends.

7. Conclusions

Pattern recognition applied to event sequences opens new doors in data analytics, enabling

prediction and anomaly detection based on hidden patterns.

We can model any tasks into a code that requires continuous improvement. Continuously

refining and updating the pattern recognition models based on new event data and feedback

from security operations, as clearly defined inside the SecOps paradigm, is the only way to

carry your activity in time.

Today, incorporating machine learning techniques that can adapt and learn from evolving

network behaviors and emerging security threats is a fundamental issue.

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