Avoid DDoS attacks

[josecelano]

I guess the worst DDoS attack you can do is to upload millions of torrents to the Index.

That can be done easily with the seeder command implemented here.

We could proposal ways to mitigate this type of attack.

[josecelano]

1. IP Rate Limit

@da2ce7 proposed an API rate limit per IPv4 or IPv6 group.

More considerations from ChatGPT:

To implement a mechanism to avoid DDoS (Distributed Denial of Service) attacks in a REST API by limiting the number of requests per IP (both IPv4 and IPv6), you can follow these general steps:

1. Choose a Rate Limiting Strategy

• Fixed Window Counting: Limit the number of requests in a fixed time window (e.g., 100 requests per minute). Resets at the end of each time window.
• Sliding Log Algorithm: Keep a log of timestamps of each request. More flexible and fair but requires more memory and computational power.
• Token Bucket Algorithm: Allows for a burst of requests up to a maximum, then refills the tokens at a steady rate.

2. Identify Clients

• Use the client's IP address to identify them. Make sure to handle both IPv4 and IPv6 addresses correctly.
• Consider using a combination of IP address and some other identifier (like API key) for more granularity.

3. Implementing the Rate Limiter

• Middleware: Implement the rate limiting as a middleware in your API stack that checks the number of requests made by an IP before processing the request.
• Distributed Storage: If your API is served by multiple servers, use a distributed cache like Redis to store and share rate limit data across servers.

4. Configure Rate Limiting

• Set limits based on your server's capacity and expected normal use.
• Customize limits for different endpoints if necessary (e.g., more sensitive or resource-heavy endpoints have stricter limits).

5. Handling Over-Limit Requests

• Return a 429 Too Many Requests HTTP status code when a client exceeds the rate limit.
• Include headers (Retry-After, for example) to inform clients when they can try again.

6. Monitoring and Adjusting

• Monitor your API's performance and adjust rate limits as necessary.
• Be prepared to quickly adjust limits in response to traffic changes or attacks.

7. Additional Security Measures

• Implement CAPTCHAs for suspicious activity.
• Use a Web Application Firewall (WAF) to provide additional security.
• Consider dynamic rate limiting based on user behavior and heuristics.

Example Code Snippet (Python Flask)

from flask import Flask, request, jsonify
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address

app = Flask(__name__)
limiter = Limiter(app, key_func=get_remote_address)

@app.route("/api/resource")
@limiter.limit("100 per minute")
def my_api():
    return jsonify({"message": "This is a rate-limited API response"})

if __name__ == "__main__":
    app.run()

Considerations

• Be aware of the implications of rate limiting on legitimate users and API consumers.
• Test the rate limiting to ensure it works as expected under various scenarios, including DDoS attacks.
• Keep an eye on evolving DDoS attack patterns and adjust your strategy as needed.

[josecelano]

2. Hashcash for HTTP

Hashcash for HTTP

I don't like the cavets:

1. Common debugging tools, such as curl do not implement Hashcash natively, meaning that more tooling is required for development.
2. In the event of a Denial of Service attack, the trade off is that the burden of the attack is shared equally among legitimate clients.
3. Any measure that makes your service less attractive or less accessible to spam bots will also do the same for some developers.

Option 1 is better because you can stop only the attacker.