In this section of the course,
we are going to discuss indication analysis.
The indication analysis section of the course
focuses on domain four, security operations.
Specifically objective 4.4.
Objective 4.4 states that given a scenario,
you must be able to analyze data and artifacts
in support of incident response activities.
Indication analysis focuses on
understanding potential security threats
by examining various components
and behaviors within a system.
This approach involves investigating digital infrastructure,
analyzing data patterns,
and scrutinizing different forms of storage
for hidden threats or anomalies.
By diving deeper into malicious software and code,
it is possible to uncover insights
about the methods the attackers use.
This comprehensive analysis helps organizations
protect critical environments,
including cloud-based systems.
It also strengthens their overall security posture
against evolving threats.
As we go through this section,
we will cover many topics related to indication analysis,
including infrastructure analysis, metadata analysis,
volatile and non-volatile storage analysis,
reverse engineering, malware analysis, code stylometry,
and the code workload protection platform.
First, we will look at infrastructure analysis.
Infrastructure analysis involves examining the hardware,
software, and network components of a system
to detect vulnerabilities or signs of compromise.
Infrastructure analysis concepts include
Joint Test Action Group or JTAG interfaces,
host analysis and network analysis.
JTAG is a hardware interface used for debugging and testing.
JTAG interfaces can be used for low-level hardware analysis
to identify vulnerabilities in embedded systems.
Next, host analysis focuses on
scrutinizing a system's endpoints, such as servers
or workstations for malware, misconfigurations,
or suspicious activity.
Finally, network analysis looks at traffic patterns,
data flows and connections
to uncover malicious communications or intrusion.
Next, we will explore metadata analysis.
Metadata analysis is examining
the underlying data about files, media or communications.
Metadata analysis is used to uncover information
about the origin, manipulation
or potential malicious intent of files,
media or communications.
Metadata analysis concepts include files and filesystems,
images and audio, and video and email header analysis.
Within files and filesystems,
metadata analysis can reveal file creation dates,
modification times, and user permissions.
This analysis can help identify tampered
or suspicious files by highlighting inconsistencies,
unauthorized changes, or unusual access patterns.
Metadata from images, audio, and video
can include information about device models,
GPS coordinates or editing history.
This analysis can assist in tracing the source of media
by identifying where and how it was created or altered.
Email headers provide details about the sender,
recipient and transmission path.
This analysis can help identify phishing attempts
or spoofed communications.
For example, an investigator might use metadata analysis
of an email header to examine the email's received fields,
which trace the path the email took
through various email servers.
By analyzing details such as the originating IP address,
the sending domain, and the message ID,
the investigator could discover that the email,
although it appears to be from a trusted source,
was actually sent from a suspicious
or malicious IP address
outside of the expected geographic region
of the organization.
After that, we will look at volatile
and non-volatile storage analysis.
Volatile and non-volatile storage analysis
examines both temporary and permanent data storage
to identify evidence of compromise or malicious activity.
Volatile and non-volatile storage analysis concepts
include the order of volatility and forensic imaging.
Volatile storage such as CPU cash holds temporary data
that is lost almost immediately
when a system is powered off.
The order of volatility prioritizes capturing
highly ephemeral data such as CPU cache first.
Non-volatile storage like hard drives,
contains permanent data that persists even after power loss
and is often analyzed through forensic imaging.
For example, during a forensic investigation,
an analyst might first capture the contents
of volatile memory to retrieve active processes,
network connections, or encryption keys
before turning to forensic imaging of non-volatile storage
to examine deleted files or logs.
Next, we will explore reverse engineering.
Reverse engineering breaks down software
or hardware components to understand their structure,
functionality, and potential vulnerabilities.
Reverse engineering concepts include byte code, binary code,
as well as disassembly and decompilation.
Byte code is a low level representation of code
that can be executed by virtual machines.
Binary refers to machine level code
that the computer directly executes,
and it is made up of ones and zeros.
Disassembly is the process
of converting binary code into assembly language
to analyze how the software operates.
Assembly language is a low level programming language
that provides a human readable representation
of a computer's machine code instructions.
Decompilation goes a step further than disassembly
and attempts to translate executable code
into higher level language for easier understanding.
For example, an analyst might reverse engineer
a piece of malware by disassembling its binary code
decompiling portions of the code
to understand its functionality
and analyzing byte code to identify any hidden routines
or malicious behaviors.
Then we will look at malware analysis.
Malware analysis is examining malicious software
to understand its behavior, impact,
and potential Indicators of Compromise.
Malware analysis concepts include sandboxing,
malware detonation, and Indicator of Compromise extractions.
Sandboxing is a technique that isolates applications
or processes in a controlled environment
to prevent them from affecting the rest of the system.
This isolation allows the safe execution
and analysis of potentially harmful code.
Sandboxing tools include Joe Sandbox and Cuckoo Sandbox.
Next, malware detonation is the process
of running the malware within the sandbox
to trigger its full functionality.
Finally, Indicator of Compromise extraction
identifies key artifacts such as file hashes, IP addresses,
and domain names that can be used
to detect or block future attacks.
For example, an analyst may use Cuckoo sandbox
to detonate suspected malware in an isolated environment
while closely monitoring its behavior.
Monitored behavior may include file modifications,
registry changes, and network communications.
By specifically observing the malware's
attempted outbound connections,
an analyst may extract malicious domain names, IP addresses,
or file hashes as Indicators of Compromise.
These indicators of compromise may then be fed
into the organization threat detection systems,
such as firewalls or intrusion detection systems
to block similar threats.
After that, we will explore code stylometry.
Code stylometry is the process
of analyzing a developer's coding style
to identify unique patterns that can be used
for malware attribution
or to trace the origin of specific software.
Code stylometry concepts include variant matching,
code similarity, and malware attribution.
Variant matching looks for similarities
between different versions
or variants of the same malware family.
In this way, variant matching helps security teams
quickly identify evolving threats
by recognizing patterns in new malware strains
from the same malware family.
Next, code similarity focuses on comparing segments of code
across multiple samples to detect shared structures,
functions, or techniques.
Finally, malware attribution uses these findings
to potentially link malware
to a specific threat actor or group
by recognizing unique coding habits or reused code.
For example, if threat hunters
identify similar coding patterns
between a new malware sample and previous attacks,
code stylometry might attribute the malware
to a known threat group, providing valuable insights
into the attacker's tactics and techniques.
By understanding the attacker's
code, style and preferences,
organizations can also improve detection systems
to spot future variants of the malware more effectively.
Finally, we will look at
the Cloud Workload Protection Platform or CWPP.
A Cloud Workload Protection Platform is a security solution
designed to detect, protect,
and respond to threats targeting cloud-based workload.
Cloud workload protection platform concepts include
detection and response, integration
and multi-cloud environments.
Detection and response capabilities
allow Cloud Workload Protection Platforms
to monitor cloud workloads in real time,
identifying suspicious activities or breaches
and enabling rapid countermeasures.
Integration refers to the platform's ability
to work seamlessly with existing cloud infrastructure
and security tool.
Supporting multi-cloud environments
ensures that workloads across
various cloud service providers
like AWS, Azure or Google Cloud are consistently protected.
For example, a Cloud Workload Protection Platform
might detect an unusually large data transfer
from an S3 bucket in an AWS environment
to an unrecognized external IP address,
flagging it as a potential data exfiltration attempt.
Cloud workload protection platform
could then trigger an automated response
to block the connection,
revoke access to the compromised user,
and isolate the affected instance.
Simultaneously, the Cloud Workload Protection Platform
could integrate with other security tools,
such as Azure Security Center
or Google Cloud's Security Command Center,
to ensure that similar suspicious activities
are monitored for and blocked across the organization's
multi-cloud infrastructure and workloads.
To finish things off, we'll take a short quiz
to see what you learned during this section of the course,
and we will review each of those quiz questions fully
to ensure you can explain why the right answers were right
and the wrong answers were wrong.
So let's get ready to dive into indication analysis
in this section of the course.