MONITOR FAILED LOGINS WITH PYTHON LANGUAGE

Monitor failed logins in your Python application

In most Python applications, user authentication is a fundamental requirement to control access, safeguard security, and prevent issues like API misuse. Authentication methods can vary, with common approaches including basic authentication, social logins (e.g., Google, Facebook), and more.

Irrespective of the chosen authentication method, dealing with failed login attempts is a frequent concern. Failures can result from incorrect credentials or other factors like login attempts by unauthorized users trying to breach the system. In such scenarios, it is essential to monitor and respond appropriately to failed login attempts. For instance, identifying users repeatedly struggling to log in may prompt us to offer assistance, while cases of brute-force attacks may require immediate actions such as IP address blocking, notifying the targeted user, and more.

Enter Palzin Track, our solution for effective monitoring and issue tracking. Palzin Track is a robust, real-time event tracking tool seamlessly integrated with Python. Setting up real-time event tracking for critical application aspects is made straightforward. Additionally, we offer advanced features for taking event tracking to the next level, including user journey creation, analytics, insights, and more.

Consider the example of handling failed logins. With Palzin Track, we can configure it to track failed login attempts and provide notifications when unusual activity is detected. This empowers us to maintain a constant vigilance over our application's security and take prompt action when necessary.

Connect Palzin Track to Python

Setting up Palzin Track

1. Sign up for a free Palzin Track account.
2. Create your first project from the dashboard.
3. Head to settings and copy your API token.

Python code snippets

To track failed logins, you can use the following code snippet Please ensure to replace YOUR_API_TOKEN with your API token and update the project and channel names.

Using Python with http.client

import http.client  


import json  


conn = http.client.HTTPSConnection("palzin.live")  


payload = json.dumps({  


 "project": "my-project",  


 "channel": "status",  


 "event": "Failed Login Attempt",  


 "description": "Detected 3 failed login attempts in the last 5 minutes",  


 "icon": "⚠️",  


 "notify": True  


})  


headers = {  


 'Content-Type': 'application/json',  


 'Authorization': 'Bearer YOUR_API_TOKEN'  


}  


conn.request("POST", "/api/v1/log", payload, headers)  


res = conn.getresponse()  


data = res.read()  


print(data.decode("utf-8"))

Using Python with Requests

import requests  


import json  


url = "https://api.palzin.live/v1/log"  


payload = json.dumps({  


 "project": "my-project",  


 "channel": "status",  


 "event": "Failed Login Attempt",  


 "description": "Detected 3 failed login attempts in the last 5 minutes",  


 "icon": "⚠️",  


 "notify": True  


})  


headers = {  


 'Content-Type': 'application/json',  


 'Authorization': 'Bearer YOUR_API_TOKEN'  


}  


response = requests.request("POST", url, headers=headers, data=payload)  


print(response.text)

Python integration details

We believe that event tracking should be simple and accessible to every developer and team. Therefore, we have worked hard to create the next generation of event-tracking tools. As a result, Palzin Track is flexible and easy to use, making it a great companion for your Python applications.

We would love to see you use Palzin Track to track every aspect of your Python application. So please give us a try and let us know what you think!

Other use-cases for Palzin Track

1. Monitor your CI/CD build status for your Python application
2. Monitor your CPU usage in your Python application
3. Monitor when database goes down in your Python application
4. Monitor high disk usage in your Python application
5. Monitor when a user changes their email address in your Python application
6. Monitor failed payments for your Python application
7. Monitor memory usage in your Python application
8. Monitor MySQL downtime in your Python application
9. Monitor when a new feature is used in your Python application
10. Monitor your Postgres downtime in your Python application
11. Monitor Redis downtime in your Python application
12. Monitor suspicious activity in your Python application
13. Monitor when a user exceeds the usage limit for your Python service
14. Monitor when a user is being rate limited in your Python application
15. Get a notification when your Python code is done executing
16. Send push notifications to your phone or desktop using Python
17. Track canceled subscriptions in your Python application
18. Track your Python cron jobs
19. Track when a file is uploaded to your Python application
20. Track when a form is submitted to your Python application
21. Track payment events via Python
22. Track user sign in events in Python
23. Monitor user signup events via Python
24. Track waitlist signup events via Python