Saudi Arabia’s cybersecurity ecosystem is regulated by the National Cybersecurity Authority, with the Essential Cybersecurity Controls (ECC) forming the baseline framework for protecting national and organizational assets.

Rather than treating ECC as a checklist, this article explores how ECC maps to real-world security operations such as SOC monitoring, SIEM logging, and incident response.

1. Why NCA ECC Matters

NCA ECC is mandatory for many Saudi organizations and critical sectors. Its objective is not just compliance, but operational cybersecurity maturity.

Unlike purely audit-focused frameworks, ECC emphasizes:

• Continuous monitoring
• Risk-driven decision making
• Incident readiness

2. ECC Domains in Simple Terms

Instead of control numbers, ECC focuses on outcomes:

• Governance & Risk Management
• Cyber Defense & Monitoring
• Access Control & Identity
• Incident Response & Recovery
• Third-Party & Cloud Security

These domains directly align with how SOC and security engineering teams operate daily.

3. Mapping ECC to Real Security Operations

From a security engineer’s perspective:

This mapping makes ECC actionable, not theoretical.

4. My Personal NCA ECC Compliance Lab

To internalize ECC concepts, I built a personal compliance lab covering:

• Asset inventory and classification
• Risk assessment mapped to ECC domains
• Logging and monitoring policies aligned with SOC workflows
• Incident response playbooks based on ECC expectations

This lab helped bridge the gap between compliance requirements and technical execution.

5. Final Thoughts

For security professionals targeting Saudi Arabia, understanding NCA ECC is not about memorizing controls ,it’s about demonstrating operational readiness.

By mapping ECC to SOC, SIEM, and incident response workflows, professionals can align themselves with how cybersecurity actually functions within Saudi organizations.

About the Author

Cybersecurity professional focused on SOC, SIEM, compliance, and offensive security.
Founder — Mohamed Basil (MindSec)

A breakdown of what happened, why so many services crashed, and how Cloudflare recovered.

On 18 November 2025, the internet had one of those chaotic mornings where everything seemed to break at once. ChatGPT froze. X (Twitter) refused to load. Countless websites started throwing 500 errors, timeouts, and strange login failures.

The culprit? Cloudflare, the backbone of a massive portion of the internet, experienced a serious internal service degradation that spiraled into a multi-hour global outage.

As someone deeply interested in cybersecurity and infrastructure reliability, I decided to break down what actually happened clearly, simply, and backed by the full timeline released during the incident.

What Caused the Cloudflare Outage?

Cloudflare reported that the outage began due to a sudden “spike in unusual traffic” hitting one of their internal services.

This spike triggered a service degradation inside Cloudflare’s core network, which then cascaded outward, affecting:

• Routing
• Security filtering
• Dashboard logins
• API calls
• CDN and caching
• Access & WARP services

Because Cloudflare sits in front of so many major platforms, the impact looked like half the internet suddenly collapsed.

Who Was Affected?

Any platform relying on Cloudflare’s network felt the hit. Some notable disruptions included:

• OpenAI / ChatGPT
• X (Twitter)
• Discord
• Ecommerce platforms
• Learning platforms
• Countless SaaS dashboards

In short: if you felt like the internet was “down,” you weren’t imagining it.

🕒 Cloudflare Incident Timeline (Complete Breakdown)

Below is the exact timeline Cloudflare published as they worked on resolving the issue. This timeline helps us understand how the outage progressed internally.

12:03 UTC — Investigating

Cloudflare reports “internal service degradation.”
Some services begin failing with intermittent errors.

12:21 UTC — Partial recovery, still unstable

Some services start to recover, but error rates remain high.

12:37 UTC — Still investigating

12:53 UTC — Still investigating

13:04 UTC — WARP disabled in London

During remediation, Cloudflare disables WARP access in London, causing connectivity issues for users there.

13:09 UTC — Issue identified

Cloudflare identifies the source of the outage and begins deploying a fix.

13:13 UTC — Access & WARP recovering

Cloudflare Access and WARP return to normal error levels.
WARP access re-enabled in London.

13:35 UTC — Working on restoring application services

13:58 UTC — Continued restoration efforts

14:22 UTC — Still working on the fix

14:34 UTC — Dashboard partially restored

Cloudflare deploys changes that restore the dashboard, but application services still affected.

14:42 UTC — Fix implemented

Cloudflare says they believe the core issue is resolved; monitoring continues.

14:57 UTC — Dashboard login issues persist

Some users still have trouble logging into the dashboard.

15:23 UTC — Ongoing monitoring

15:40 UTC — Post-fix stabilization

Cloudflare continues working through remaining issues after the fix.

So What Actually Happened Internally?

Based on the official statements and incident behavior:

• One internal service experienced a sudden abnormal traffic spike
• That led to service degradation
• Which cascaded into routing failures and high error rates
• Causing widespread outages across customers
• Fixes were rolled out in phases
• Some regions were impacted more than others (like London’s WARP issue)

This pattern is consistent with either:
✔ an internal system fault
✔ a misconfigured update
✔ or a traffic surge (possibly accidental, not necessarily malicious)

Cloudflare has not yet confirmed the root cause beyond “unusual traffic.”

Why This Outage Was So Big

Cloudflare is not just a CDN-it’s part of the backbone of modern internet traffic.

When Cloudflare breaks:

• DNS breaks
• CDN caching breaks
• API gateways break
• Web application firewalls break
• Authentication flows break
• Edge routing breaks

So even a small issue multiplies into a global chain reaction.

Why This Matters (Especially for Cybersecurity Professionals)

Outages like this are important real-world case studies. They highlight:

• How fragile global infrastructure is
• Why redundancy is not always enough
• How single points of failure can exist at massive scale
• The importance of anomaly detection and traffic monitoring
• How misconfigurations can cascade globally

Understanding incidents like this makes you a stronger security analyst these are the same scenarios you’ll see in interviews, post-mortems, and real-world SOC operations.

Final Thoughts

Today’s Cloudflare outage is another reminder that even the biggest, most resilient networks can stumble when unexpected conditions hit at the wrong time. The good news is that Cloudflare moved quickly, communicated consistently, and restored services in phases.

If you found this useful, feel free to follow for more breakdowns related to cybersecurity, outages, cloud security, and digital infrastructure.

Suricata is an open-source IDS/IPS and network monitoring engine widely used in enterprise security. I installed, configured, and tested Suricata on a Linux machine, integrated it with Wazuh and SIEM tools, and created real-world detection and prevention rules.

This post details the step-by-step labs I performed, including commands, configurations, and results.

1. Install Suricata and Verify Rule Loading

First, I installed Suricata:

sudo apt update
sudo apt install suricata jq -y

Verify the configuration and rules:

sudo suricata -T -c /etc/suricata/suricata.yaml

I also confirmed the default logging location:

cat /etc/suricata/suricata.yaml | grep default-log-dir
# Default: /var/log/suricata

2. Capture Live Alerts in IDS Mode

I ran Suricata in IDS mode to monitor network traffic:

sudo suricata -c /etc/suricata/suricata.yaml -i eth0

To see live alerts in JSON format:

tail -f /var/log/suricata/eve.json | jq .

Then, I tested with a simple ping:

ping -c 2 8.8.8.8

If ICMP rules are enabled, you’ll see alerts logged in eve.json.

3. Write a Rule to Detect Visits to YouTube

Custom rules are where Suricata shines. I created a rule in /etc/suricata/rules/local.rules:

alert http any any -> any any (msg:"YouTube Access Detected"; content:"youtube.com"; http_host; sid:1000002; rev:1;)

Update the Suricata config to include local.rules if not already:

# In suricata.yaml:
rule-files:
  - suricata.rules
  - local.rules

Reload rules without restarting the engine:

sudo kill -USR2 $(pidof suricata)

Test by visiting YouTube:

curl -I https://www.youtube.com

Check alerts:

jq 'select(.alert)' /var/log/suricata/eve.json | grep "YouTube"

4. Block Traffic to Facebook in IPS Mode

Switch Suricata to IPS mode using NFQUEUE:

sudo iptables -I FORWARD -j NFQUEUE --queue-num 0
sudo suricata -c /etc/suricata/suricata.yaml --af-packet

Create a blocking rule:

drop http any any -> any any (msg:"Blocking Facebook Access"; content:"facebook.com"; http_host; sid:1000003; rev:1;)

Reload rules and test:

sudo kill -USR2 $(pidof suricata)
curl -I https://www.facebook.com

The connection will be blocked, and Suricata logs will record a drop event.

5. Analyze Suricata Logs and Extract Top Alerts

Suricata generates rich logs in eve.json. I used jq and awk to extract and analyze alerts:

Top triggered rules:

jq -r 'select(.alert) | .alert.signature' /var/log/suricata/eve.json | sort | uniq -c | sort -nr | head

Top source IP addresses:

jq -r 'select(.alert) | .src_ip' /var/log/suricata/eve.json | sort | uniq -c | sort -nr | head

Top destination ports:

jq -r 'select(.alert) | .dest_port' /var/log/suricata/eve.json | sort | uniq -c | sort -nr | head

These insights helped prioritize alerts and identify the most common events in my lab.

6. Integrate Suricata with ELK Stack or Splunk

ELK Integration

I configured Filebeat to ship Suricata logs to ELK:

sudo apt install filebeat -y
sudo nano /etc/filebeat/filebeat.yml

Added this section to monitor Suricata logs:

- type: log
  paths:
    - /var/log/suricata/eve.json
  json.keys_under_root: true
  json.add_error_key: true

Start Filebeat and view logs in Kibana:

sudo systemctl enable filebeat
sudo systemctl start filebeat

Splunk Integration

Alternatively, I configured Splunk Universal Forwarder:

/opt/splunkforwarder/bin/splunk add monitor /var/log/suricata/eve.json
/opt/splunkforwarder/bin/splunk restart

In Splunk, I built dashboards showing:

• Top alerts by signature
• Top source IPs
• Alert trends over time

7. Bonus: Integration with Wazuh

Finally, I added Suricata events into Wazuh for centralized security visibility.

Install the Wazuh agent and configure Suricata log monitoring in ossec.conf:

<localfile>
  <log_format>json</log_format>
  <location>/var/log/suricata/eve.json</location>
</localfile>

Restart the agent:

sudo systemctl restart wazuh-agent

Now, Suricata alerts are visible in the Wazuh dashboard for correlation and incident response.

Key Results

Through these labs, I successfully:

• Installed and configured Suricata on Linux.
• Captured live alerts in IDS mode.
• Wrote custom detection rules (e.g., YouTube).
• Blocked traffic with IPS mode (e.g., Facebook).
• Analyzed logs using command-line tools.
• Integrated Suricata with ELK, Splunk, and Wazuh for centralized monitoring.

This project demonstrates end-to-end mastery of Suricata and its use in real-world security operations.

“Hacking the future starts with understanding your network.”

Whether you’re a beginner stepping into the world of network reconnaissance or a seasoned pen-tester auditing your environment, Nmap (Network Mapper) remains one of the most powerful tools in your cybersecurity arsenal.

In this article, I’ll walk you through not just how to use Nmap, but how I’ve mastered it ,exploring its depth from basic scans to evasion techniques, advanced NSE scripting, real-world use cases, and practical lab environments.

Why Master Nmap?

Nmap isn’t just a port scanner. It’s a comprehensive network discovery and security auditing tool that provides insight into:

• Open/closed/filtered ports
• Services and their versions
• OS detection
• Scripted vulnerability scanning
• Firewall evasion
• Network mapping

If you want to become a reliable ethical hacker or a capable defender, Nmap proficiency is non-negotiable.

Setup: My Preferred Lab Environment

I run Nmap in a Kali Linux VM (bridged mode) for complete functionality and full control. Alternatively, Ubuntu also works well.

To scan real networks responsibly, I use:

• A local virtual lab (Windows, Linux VMs on VirtualBox/VMware)
• HTB & TryHackMe machines

Pro Tip: Always scan targets you own or have permission to test.

Installing the Latest Nmap from Source (Linux)

sudo apt update
sudo apt install g++ libssl-dev
wget https://nmap.org/dist/nmap-7.95.tar.bz2
tar -xvjf nmap-7.95.tar.bz2
cd nmap-7.95/
./configure
make
sudo make install
nmap -v

Result:

Nmap 7.95 ( https://nmap.org )

My Essential Nmap Usage Patterns

Basic Host Discovery

nmap -sn 192.168.1.0/24

Ping sweep to detect live hosts (no port scan).

Fast Scan

nmap -F 192.168.1.0/24

Scans top 100 common ports only (faster recon).

Top Ports Scan

nmap --top-ports 1000 192.168.1.10

Scan the most commonly used 1000 ports.

All Port Scan

nmap -p- 192.168.1.10

Scan all 65535 TCP ports.

Service & Version Detection

nmap -sV -p 22,80,443 192.168.1.10

Identifies services and their versions on specific ports.

Default Script Scan

nmap -sC 192.168.1.10

Uses a default set of scripts for quick insights (like HTTP titles, SSL info, etc.).

Aggressive Scan

nmap -A 192.168.1.10

Combines OS detection, version, script scan, and traceroute.

OS Fingerprinting

nmap -O 192.168.1.10

Attempts to detect the target operating system.

Vulnerability Scanning

nmap --script vuln 192.168.1.10

Uses built-in NSE scripts to find known vulnerabilities.

Advanced Scanning & Optimization Flags

Scan Speed Control

-T0 to -T5

Timing template: T0 (Paranoid) to T5 (Insane) — higher means faster but noisier.

Rate Control

--min-rate 1000 --max-rate 5000

Sets min/max packets per second — helps with slow/fast scans.

Port Ratio

--port-ratio 0.9

Scan ports with likelihood ≥90% of being open (based on profile data).

Reason Flag

--reason

Show why Nmap considers a port open (e.g., SYN-ACK received).

Traceroute

--traceroute

Adds traceroute to scan results — shows network path.

Packet Trace

--packet-trace

Shows raw packet send/receive during the scan (for debugging or learning).

Firewall Evasion & Stealth Techniques

nmap -sS -Pn -T2 -f --data-length 1337 -D 192.168.1.100,ME

• -sS: SYN scan (stealthy)
• -Pn: No ping, assume target is up
• -f: Fragment packets
• --data-length: Adds padding to packets
• -D: Adds decoys to mask your real IP

TCP/IP Header Manipulation (Advanced)

nmap --source-port 53 --ip-options "R" --ttl 133

• --source-port: Sets source port (e.g., DNS port 53 to bypass firewalls)
• --ip-options: Insert custom IP options (e.g., "R" for record route)
• --ttl: Time To Live manipulation (can fool some firewalls)

Output & Reporting

nmap -oA internal_scan -sV 192.168.1.10

Generates:

• internal_scan.nmap: Human-readable
• internal_scan.xml: XML for tools
• internal_scan.gnmap: Greppable output

Also try:

-oX output.xml      # Only XML
-oG output.gnmap    # Only grepable
-oN output.txt      # Plain text

My Real-World Nmap Combos (from Muscle Memory)

nmap -sC -sV -O -T4 -p- 10.10.10.10
nmap --script vuln -p 80,443 10.10.10.10
nmap -sS -Pn -T3 --top-ports 1000 192.168.1.0/24
nmap -F --reason --traceroute 192.168.1.10

Real-world Use Cases

Penetration Testing: Find pivot points and exposed services
CTFs & Hack The Box: Always the first recon step automated with bash wrappers
Security Audits: Build asset inventory, find unpatched software
Firewall Evasion: Stay stealthy and bypass filters
Debugging Networks: Use --packet-trace and --reason for deep visibility

Going Beyond: Nmap + Automation

I’ve also used:

• Masscan + Nmap for rapid port discovery followed by deep analysis
• Python scripts to automate NSE scanning
• Custom NSE scripts to extend functionality

Bonus: Zenmap for Visual Scans

While I prefer CLI, Zenmap provides:

• Pre-configured scan profiles
• Network topology maps
• Exportable reports

Understanding Results

Port State Meaning Open Service is actively accepting connections Closed No service, but host responds Filtered Packet was dropped (likely by firewall)

With Wireshark + Nmap, I’ve analyzed SYN, RST, and dropped packets to understand deeper behaviors of firewalled targets.

Final Thoughts

Nmap is not just a scanner; it’s a network exploration platform.

I don’t just know how to use Nmap ,I understand it inside-out, from packet level behaviors to stealth techniques, script debugging, and integration into real attack workflows.

Let’s connect:
LinkedIn: Mohamed Basil
GitHub: Basil Mellow
Tagline: Hacking the Future with Nmap & Beyond.

Introduction

Digital forensics plays a crucial role in modern incident response. Whether you’re investigating insider threats, data exfiltration, or unauthorized access, tools like Autopsy empower analysts to reconstruct events with forensic precision.

In this case study, I showcase my forensic investigation of the well-known M57 Jean Case using Autopsy on Linux. This investigation covers metadata analysis, file system inspection, user activity tracking, and reporting everything needed to demonstrate practical DFIR skills.

Tools Used

• Autopsy (Linux version)
• The Sleuth Kit (TSK backend)
• Ubuntu Linux 22.04
• M57 Jean Disk Image (from Digital Corpora)
  Download Link

Case Scenario -The M57 Jean Incident

M57 is a fictional company that suspects employee Jean of unauthorized activities on her workstation, including:

• Downloading confidential documents
• Using personal USB drives
• Communicating with a competitor

The company took a forensic image of her system and tasked us with uncovering the truth.

Step-by-Step Autopsy Workflow (Linux)

Step 1: Install Autopsy on Linux

sudo apt update
sudo apt install autopsy sleuthkit

Launch Autopsy in the browser using autopsy command and follow the link shown (usually http://localhost:9999/autopsy)

Step 2: Create a New Case

• Case Name: M57_Jean_Investigation
• Base Directory: ~/autopsy-cases
• Case Number: M57-2025
• Examiner Name: Mohamed Basil

Step 3: Add Disk Image

• Go to Add Data Source
• Select Disk Image or VM File
• Browse to jean.raw from the M57 dataset
• Enable key ingest modules:
• File Type Identification
• Recent Activity
• Data Carving
• Web Artifacts
• Hash Lookup

Step 4: Analyze the File System

Autopsy automatically parses the image. I navigated the file system and discovered:

• Business documents injean ~/Documents/M57-PatentStrategy.docx
• Deleted PDFs with sensitive terms
• Suspicious folders in /home/jean/.mozilla/

Step 5: Keyword Search

I used custom keyword lists to search for:

• "Confidential"
• "Acquisition"
• "M57 Strategy"

Results revealed emails and document references suggesting unauthorized knowledge transfer.

Step 6: Web Activity

Using the Web Artifacts module, I found:

• Access to personal Gmail
• Visits to job portals
• Download of compression tools (e.g., 7zip, tar.gz archives)

These activities hinted at possible file packaging and exfiltration.

Step 7: Report Generation

Autopsy’s Report tab was used to generate:

• HTML Report: For executive summary
• Detailed Artifact Report: For technical documentation
• Screenshots: Of search hits, file paths, and timelines

All reports and screenshots are available on my GitHub:
GitHub Repository

Sample Findings

Evidence Type Artifact Found Relevance Deleted Document M57-PatentStrategy.docx (Recovered) Confidential business file USB Metadata Kingston_123_USB Potential exfiltration method Web History gmail.com, job sites Unauthorized access and intent File Timestamps Mismatched modify/delete times Possible anti-forensics attempt

What I Learned

• Autopsy runs efficiently on Linux with Sleuth Kit
• File metadata and timeline analysis are core to attribution
• Even deleted files can still tell a story
• Browser artifacts are key in insider threat cases

Why This Project Matters

This case study simulates a real-world DFIR engagement and highlights my ability to:

• Use open-source tools in a Linux forensic lab
• Follow a structured analysis methodology
• Produce technical and executive reports
• Communicate findings clearly to stakeholders

It aligns with job responsibilities in SOC, DFIR, and Threat Intel teams.

GitHub Repository

🔗 View the Project Files, Screenshots, and Report Templates

Includes:

• Autopsy screenshots
• Forensic report templates (DOCX & PDF)
• File Type
• Deleted document hashes

Final Thoughts

Autopsy is a powerful tool for digital detectives. With the M57 Jean case, I was able to uncover hidden truths using only Linux tools -demonstrating practical incident investigation skills.

If you’re building a DFIR team or training future responders -this kind of exercise is a must.

MindSec-Hacking the Future

Connect on LinkedIn

Splunk is far more than just a log search engine — it’s a security operations platform. In this post, I’ll walk through how I built a fully functional blue-team-centric Splunk lab with production-style use cases, including:

• Efficient log ingestion and filtering
• Alerting and threat detection
• Index optimization and retention
• Threat intelligence enrichment
• Custom dashboards and data correlation

This walkthrough is suitable for anyone preparing for a SOC role, working with SIEM tools, or aiming to use Splunk beyond just basic dashboards.

Lab Architecture:

• Splunk Enterprise (local instance) on Ubuntu VM
• Forwarders: Universal Forwarder on Windows and Linux
• Log Sources:
• Windows Event Logs (4625, 4624, 4672)
• Linux auth.log, secure.log
• Simulated Apache and Firewall logs
• Custom threat feed CSV (public threat intel)

Log Ingestion & Filtering

1. Index Setup (indexes.conf):

[win_logs]
homePath = $SPLUNK_DB/win_logs/db
coldPath = $SPLUNK_DB/win_logs/colddb
thawedPath = $SPLUNK_DB/win_logs/thaweddb
frozenTimePeriodInSecs = 2592000

[linux_logs]
homePath = $SPLUNK_DB/linux_logs/db
coldPath = $SPLUNK_DB/linux_logs/colddb
thawedPath = $SPLUNK_DB/linux_logs/thaweddb
frozenTimePeriodInSecs = 2592000

2. Dropping Noisy Logs at Ingestion (props.conf + transforms.conf):

# props.conf
[linux_logs]
TRANSFORMS-null_debug = setnull

# transforms.conf
[setnull]
REGEX = DEBUG
DEST_KEY = queue
FORMAT = nullQueue

This eliminates unnecessary debug logs from ever being indexed.

Alerting & Detection Rules

1. Brute-force SSH Detection:

index=linux_logs EventCode=FAILED_SSH
| stats count by src_ip, user
| where count > 5

2. Admin Login Alert (Windows):

index=win_logs EventCode=4672
| table _time, Account_Name, host

3. Suspicious Login from Foreign IP:

index=win_logs OR index=linux_logs EventCode=4624 OR EventCode=22
| lookup geolocation.csv ip as src_ip OUTPUT country
| where country!="India"

Threat Intelligence Integration

Custom Threat Lookup:

# threatlist.csv
malicious_ip
45.10.20.3
13.67.98.1

Enrichment SPL:

index=linux_logs OR index=win_logs
| lookup threatlist.csv malicious_ip as src_ip OUTPUT malicious_ip AS matched_ip
| where isnotnull(matched_ip)
| table _time, src_ip, matched_ip, host, user

Dashboard Overview

• Failed Login Dashboard: SSH/Windows login failures by IP/user
• Threat Intel Match: Any log matched against threat feed
• Login GeoMap: Visualize logins by region
• Index Volume Tracker: Monitor daily growth per index

Index Management Strategy

Storage Controls:

• frozenTimePeriodInSecs per index
• Summary indexing for daily event counts
• NullQueue filtering for DEBUG, TRACE logs

Summary Index SPL:

index=linux_logs EventCode=FAILED_SSH
| stats count by src_ip
| collect index=summary_failed_ssh

Conclusion:

This lab simulates real-world Splunk usage in blue team operations — from ingestion to analysis, with retention and performance controls built-in. These setups make Splunk more efficient, scalable, and security-aware.