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This project implements a fully automated Cyber Incident Response Dashboard with the following features:
– Incident collection via SecureX and Fortinet APIs
– Threat prediction using Artificial Intelligence
– Professional PDF report generation
– Automated email scheduling for report delivery
– Threat type and period filtering
– Scalable database (PostgreSQL)
You Should Know:
1. SecureX API Integration
SecureX is Cisco’s cloud-native security platform. Below is a Python example to fetch incidents:
import requests
url = "https://securex.api.cisco.com/v1/incidents"
headers = {
"Authorization": "Bearer YOUR_ACCESS_TOKEN",
"Content-Type": "application/json"
}
response = requests.get(url, headers=headers)
incidents = response.json()
print(incidents)
2. Fortinet API for Threat Data
Fortinet’s FortiGate API can be used to extract logs:
curl -X GET "https://<fortigate-ip>/api/v2/monitor/system/ips/events" -H "Authorization: Bearer YOUR_API_KEY"
3. AI Threat Prediction with Python (Scikit-learn Example)
A simple ML model for threat classification:
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
Sample dataset (replace with real threat data)
data = pd.read_csv("threat_data.csv")
X = data.drop("threat_level", axis=1)
y = data["threat_level"]
model = RandomForestClassifier()
model.fit(X, y)
Predict new threats
prediction = model.predict([[bash]])
print(f"Threat Level: {prediction}")
4. Automated PDF Reporting with ReportLab
Generate PDF reports in Python:
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
def generate_pdf(filename, incidents):
c = canvas.Canvas(filename, pagesize=letter)
c.drawString(100, 750, "Cyber Incident Report")
for i, incident in enumerate(incidents):
c.drawString(100, 700 - (i 20), f"Incident {i+1}: {incident['description']}")
c.save()
generate_pdf("incident_report.pdf", incidents)
5. PostgreSQL Database Setup
Store incidents in a scalable PostgreSQL DB:
CREATE TABLE incidents ( id SERIAL PRIMARY KEY, description TEXT, threat_level VARCHAR(50), timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP );
6. Automated Email Scheduling with Cron
Use `cron` to schedule report emails:
0 9 /usr/bin/python3 /path/to/send_report.py
7. Linux Log Analysis for Incident Detection
Use `grep` and `awk` to analyze logs:
grep "Failed password" /var/log/auth.log | awk '{print $1, $2, $3, $9, $10}'
8. Windows Event Log Extraction
Use PowerShell to extract security logs:
Get-WinEvent -LogName Security -MaxEvents 50 | Where-Object {$_.ID -eq 4625}
What Undercode Say:
Automating cyber incident response with AI and scalable databases significantly improves threat detection efficiency. Combining SecureX, Fortinet APIs, and machine learning allows real-time threat prediction. Automated reporting and email alerts ensure rapid response. PostgreSQL ensures data scalability, while Linux/Windows log analysis aids in forensic investigations.
Expected Output:
- SecureX API incident data in JSON format
- Fortinet threat logs
- AI-predicted threat classifications
- Generated PDF reports
- PostgreSQL database entries
- Scheduled email alerts
- Linux/Windows log analysis results
References:
Reported By: Fabiano Meda – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
