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Cloud Forensics Demystified

You're reading from  Cloud Forensics Demystified

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781800564411
Pages 384 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Ganesh Ramakrishnan Ganesh Ramakrishnan
Profile icon Ganesh Ramakrishnan
Mansoor Haqanee Mansoor Haqanee
Profile icon Mansoor Haqanee
View More author details

Table of Contents (18) Chapters

Preface 1. Part 1: Cloud Fundamentals
2. Chapter 1: Introduction to the Cloud 3. Chapter 2: Trends in Cyber and Privacy Laws and Their Impact on DFIR 4. Chapter 3: Exploring the Major Cloud Providers 5. Chapter 4: DFIR Investigations – Logs in AWS 6. Part 2: Forensic Readiness: Tools, Techniques, and Preparation for Cloud Forensics
7. Chapter 5: DFIR Investigations – Logs in Azure 8. Chapter 6: DFIR Investigations – Logs in GCP 9. Chapter 7: Cloud Productivity Suites 10. Part 3: Cloud Forensic Analysis – Responding to an Incident in the Cloud
11. Chapter 8: The Digital Forensics and Incident Response Process 12. Chapter 9: Common Attack Vectors and TTPs 13. Chapter 10: Cloud Evidence Acquisition 14. Chapter 11: Analyzing Compromised Containers 15. Chapter 12: Analyzing Compromised Cloud Productivity Suites 16. Index 17. Other Books You May Enjoy

Logging Dataflow pipelines

Dataflow pipelines provide a stream of data or batch processing capabilities at scale. GCP’s Dataflow pipeline is based on Apache Beam. Logs can be streamed at variable volumes in near real time using Dataflow applications.

Any actions performed on GCP Dataflow are recorded by default in Logs Explorer. Through Logs Explorer, investigators can detect any changes to the Dataflow parameters or whether unauthorized users altered the pipeline.

Note that a Docker instance forms the base of any Dataflow pipeline’s operations. Therefore, investigators must also investigate the logs emitted by the GKE cluster and GCE Instance Group Manager. GCP relies on Instance Group Manager to create multiple managed VMs that run the containers (GKE) to handle instance resourcing and deploying VMs automatically.

The following figure outlines some sample resources required for successful Dataflow pipeline execution. Like Syslog, Dataflow events are tagged...

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