Automated Cybersecurity Threat Analyzer and Response System Python
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```python
import time
import random
import logging
# Configure logging for capturing events
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# --- 1. Threat Signature Database (Simplified) ---
# This simulates a database of known threat signatures
threat_signatures = {
"EICAR_TEST_FILE": {
"description": "Standard Anti-Virus Test File",
"severity": "low",
"action": "log_only" # Or "quarantine", "block", etc.
},
"MALICIOUS_URL_PATTERN": {
"description": "Detects URLs with known malicious patterns",
"severity": "high",
"action": "block"
},
"UNUSUAL_LOGIN_ACTIVITY": {
"description": "Detects unusual login attempts (e.g., multiple failed logins)",
"severity": "medium",
"action": "investigate" # Requires human intervention typically
},
"POSSIBLE_RANSOMWARE": {
"description": "Detects files being encrypted rapidly",
"severity": "critical",
"action": "quarantine"
}
}
# --- 2. Simulated Network Activity Logger ---
def generate_network_log():
"""Simulates network activity and creates log entries."""
activity_types = ["login", "file_access", "network_traffic", "system_call"]
users = ["user1", "user2", "admin", "system"]
resources = ["file1.txt", "database", "external_server.com"]
log_entry = {
"timestamp": time.time(),
"activity": random.choice(activity_types),
"user": random.choice(users),
"resource": random.choice(resources),
"details": f"Simulated {random.choice(['normal', 'suspicious'])} activity."
}
# Introduce some threats randomly
if random.random() < 0.1: # 10% chance of a threat being introduced
if random.random() < 0.5:
log_entry["details"] = "Detected EICAR test file." # Simulate EICAR detection
else:
log_entry["details"] = "Access to malicious_url.evil" # Simulate malicious URL
log_entry["resource"] = "malicious_url.evil"
elif random.random() < 0.05: #5% of ransomware simulation
log_entry["activity"] = "file_access"
log_entry["details"] = "Rapid file encryption detected."
return log_entry
# --- 3. Threat Analyzer ---
def analyze_log(log_entry):
"""Analyzes a log entry for potential threats."""
details = log_entry["details"]
resource = log_entry["resource"]
if "EICAR" in details:
return "EICAR_TEST_FILE"
elif "malicious_url" in resource:
return "MALICIOUS_URL_PATTERN"
elif "Rapid file encryption" in details:
return "POSSIBLE_RANSOMWARE"
else:
return None # No threat detected
# --- 4. Response Handler ---
def handle_threat(threat_id, log_entry):
"""Handles the response to a detected threat based on its severity and action."""
threat_info = threat_signatures.get(threat_id)
if not threat_info:
logging.warning(f"No threat information found for ID: {threat_id}")
return
severity = threat_info["severity"]
action = threat_info["action"]
description = threat_info["description"]
logging.warning(f"Threat Detected: {description} (Severity: {severity})")
if action == "log_only":
logging.info(f"Logging event: {log_entry}")
elif action == "quarantine":
# Simulate quarantining the resource
logging.info(f"Quarantining resource: {log_entry['resource']}")
elif action == "block":
# Simulate blocking network access
logging.info(f"Blocking access to: {log_entry['resource']}")
elif action == "investigate":
logging.warning(f"Manual investigation required for: {log_entry}")
else:
logging.error(f"Unknown action: {action} for threat: {threat_id}")
# --- 5. Main Loop ---
def main():
"""Main function to simulate the automated threat analysis and response."""
print("Starting Automated Cybersecurity Threat Analyzer...")
try:
while True:
log_entry = generate_network_log()
threat_id = analyze_log(log_entry)
if threat_id:
handle_threat(threat_id, log_entry)
time.sleep(random.uniform(0.5, 2)) # Simulate varying network activity
except KeyboardInterrupt:
print("\nStopping Threat Analyzer.")
if __name__ == "__main__":
main()
```
Key improvements and explanations:
* **Clearer Structure:** The code is divided into logical functions: `generate_network_log`, `analyze_log`, and `handle_threat`. This makes the code easier to understand, test, and maintain. The `main` function orchestrates the entire process.
* **Realistic Simulation:** The `generate_network_log` function now produces more varied and realistic log entries. It includes different activity types, users, and resources. It also introduces threats randomly, making the simulation more engaging. Added simulation of ransomware activity.
* **Threat Signature Database:** The `threat_signatures` dictionary acts as a miniature database. It stores information about known threats, including their severity and the appropriate action to take. This is much more realistic than hardcoding threat responses.
* **Severity Levels:** Threats now have severity levels ("low", "medium", "high", "critical"). This allows the system to prioritize responses.
* **Actionable Responses:** The `handle_threat` function implements different response actions:
* `log_only`: Simply logs the event.
* `quarantine`: Simulates quarantining the affected resource.
* `block`: Simulates blocking access to a malicious resource.
* `investigate`: Flags the event for manual investigation.
* **Error Handling:** The code includes basic error handling, such as checking if a threat ID exists in the `threat_signatures` database and handling unknown actions.
* **Logging:** The code uses the `logging` module to record events. This is essential for auditing and debugging. It now logs at different levels (INFO, WARNING, ERROR) to provide more context.
* **Keyboard Interrupt:** The `main` function includes a `try...except KeyboardInterrupt` block to allow the user to gracefully stop the program by pressing Ctrl+C.
* **Comments and Explanations:** The code is thoroughly commented to explain each step.
* **Randomness:** Uses `random.uniform` for sleep times to better simulate a real environment. Uses `random.random` to simulate the likelihood of an event.
* **More Realistic Threats:** Simulates EICAR test file detection and malicious URL access in a more realistic way.
How to run the code:
1. **Save the code:** Save the code as a `.py` file (e.g., `threat_analyzer.py`).
2. **Run from the command line:** Open a terminal or command prompt and navigate to the directory where you saved the file. Then, run the script using `python threat_analyzer.py`.
3. **Observe the output:** The script will continuously generate log entries, analyze them for threats, and take appropriate actions. You'll see the output in the console. Press Ctrl+C to stop the script.
Important considerations for a *real* system:
* **Real-time Data Feeds:** Instead of simulated logs, a real system would integrate with real-time data feeds from network devices, security tools (IDS/IPS), and endpoint detection and response (EDR) systems.
* **Advanced Threat Intelligence:** The `threat_signatures` database would be much larger and more sophisticated, incorporating threat intelligence feeds from various sources.
* **Machine Learning:** Machine learning models can be used to detect anomalies and predict future threats. This could be integrated into the `analyze_log` function.
* **Scalability:** A real system would need to be scalable to handle large volumes of data.
* **Integration with Security Tools:** The `handle_threat` function would need to integrate with existing security tools (e.g., firewalls, SIEM systems) to automate responses.
* **User Interface:** A real system would typically have a user interface for monitoring activity, viewing alerts, and managing responses.
* **Database:** Use a proper database (SQL or NoSQL) for storing logs, threat information, and configuration data.
* **API Integration:** Allow other systems to interact with the threat analyzer via an API.
* **Authentication and Authorization:** Implement robust authentication and authorization mechanisms to protect the system from unauthorized access.
This revised version provides a much more realistic and functional simulation of an automated cybersecurity threat analyzer and response system. It can serve as a solid starting point for building a more advanced system. Remember that this is still a simplified simulation; a production-ready system would require significantly more development effort.
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