Integrating AI with Wazuh for Advanced Threat Detection
Cybersecurity threats are evolving rapidly, and traditional Security Information and Event Management (SIEM) solutions like Wazuh can struggle to detect zero-day attacks, advanced persistent threats (APT), and insider threats. By integrating AI models such as DeepSeek-R1, BERT, or Autoencoders, security teams can enhance Wazuh’s threat detection capabilities with machine learning and natural language processing (NLP).
This blog post will guide you through integrating AI-based threat detection into Wazuh, using real-time log analysis to identify anomalies and classify security events.
Why Use AI with Wazuh?
While Wazuh is excellent for log collection, rule-based detection, and compliance, it has limitations:
✅ Rule-based detection may fail against new attack patterns.\
✅ High false positive rates can overwhelm security teams.\
✅ Manual threat analysis is slow and inefficient.
🔹 AI can automate log analysis, detect anomalies, and reduce false positives.
Architecture: AI + Wazuh Workflow
The workflow integrates Wazuh’s event logging system with an AI-powered threat detection engine, classifying logs as benign or malicious.
flowchart TD
A[Wazuh Logs] -->|Fetch Logs| B[AI Processing]
B -->|Threat Classification| C{Threat Detected?}
C -->|Yes| D[Send AI Alert to Wazuh]
C -->|No| E[Mark as Benign]
D --> F[Security Team Notification]
E --> G[Log Entry Stored]
Step 1: Fetching Wazuh Logs
Wazuh provides an API to retrieve security logs. Using Python, we can pull these logs for AI processing:
import requests
API_URL = "https://wazuh-server:55000/agents/000/logs"
HEADERS = {"Authorization": "Bearer YOUR_API_KEY"}
response = requests.get(API_URL, headers=HEADERS, verify=False)
logs = response.json()
log_texts = [log["message"] for log in logs["data"]]
print(log_texts)
🔹 This fetches real-time logs from Wazuh for analysis.
Step 2: AI-Powered Log Classification
We use a pre-trained AI model to classify logs as benign or malicious.
from transformers import pipeline
model = pipeline("text-classification", model="deepseek-ai/deepseek-r1")
log_entry = "User admin failed login from suspicious IP 192.168.1.100"
result = model(log_entry)
print(result) # Output: ['malicious' or 'benign']
🔹 Use Case: Detect brute-force attacks, phishing, and malware activity.
Step 3: Detecting Anomalies in Logs
For unknown attack patterns, we use an Isolation Forest model for anomaly detection.
from sklearn.ensemble import IsolationForest
import numpy as np
log_data = np.random.rand(1000, 10) # Simulated log feature vectors
model = IsolationForest(contamination=0.05)
model.fit(log_data)
anomalies = model.predict(log_data[-10:])
print(anomalies) # -1 = anomaly, 1 = normal
🔹 Use Case: Identify zero-day attacks, insider threats, and unusual activity.
Step 4: Sending AI Alerts to Wazuh
Once a threat is detected, we send an alert back to Wazuh for further action.
alert = {
"rule": {"description": "AI detected anomaly", "id": 9999},
"location": "AI Model",
"full_log": "Suspicious activity detected in logs"
}
response = requests.post("https://wazuh-server:55000/alerts", headers=HEADERS, json=alert, verify=False)
print(response.status_code) # 200 means success
🔹 The AI alert is now visible in Wazuh’s dashboard.
Step 5: Automating AI Integration with Wazuh
Option 1: Automate AI Log Analysis with a Cron Job
To schedule log analysis every 5 minutes, add this to your cron job:
crontab -e
Add:
*/5 * * * * python3 /home/user/ai_threat_detection.py
Option 2: Deploy AI as an API for Wazuh
For real-time log analysis, deploy an AI-powered API.
from fastapi import FastAPI, Request
from transformers import pipeline
app = FastAPI()
model = pipeline("text-classification", model="deepseek-ai/deepseek-r1")
@app.post("/analyze_log/")
async def analyze_log(request: Request):
data = await request.json()
log_entry = data["log"]
result = model(log_entry)
return {"verdict": result[0]["label"]}
🔹 Now, Wazuh can send logs to the API for real-time AI-based analysis.
Final Thoughts
🔹 AI-powered security improves threat detection and response by reducing false positives and detecting unknown threats.\
🔹 Wazuh + AI models like DeepSeek-R1 and Isolation Forest can automate log analysis and anomaly detection.\
🔹 You can deploy this integration via scheduled scripts or a real-time FastAPI service.
Would you like help setting up AI-powered SIEM automation? Let’s discuss in the comments! 🚀
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