Benchmarking a hybrid threat detection system (backend + APIs)
Posted by Emergency-Rough-6372@reddit | Python | View on Reddit | 16 comments
I’ve been spending some time reading through discussions here and I genuinely like how people break things down and share practical perspectives, so I thought I’d put this out as more of a discussion than a direct “help” post.
Lately I’ve been working on a backend system focused on detecting potential threats in API flows and chatbot interactions. It’s not purely rule-based, it combines deterministic security checks (using established open-source libraries) with a probabilistic layer for risk scoring and decision-making.
Because of that mix, evaluation becomes a bit tricky. It’s not a clean input → output system, and correctness isn’t always binary.
What I’ve been thinking about is how people approach benchmarking in cases like this. When part of the system is deterministic and part is probabilistic, what does “good performance” actually look like?
Is it more about:
- precision/recall on known attack patterns?
- calibration of risk scores?
- false positive vs false negative trade-offs?
- consistency over time?
Another thing I’ve been running into is edge cases. With deterministic checks, it’s straightforward. But once you add a probabilistic layer, it feels more like you’re evaluating behavior over distributions rather than validating exact outputs.
Since I’m relying on well-established libraries for the core detection logic, the challenge isn’t verifying those individually ,it’s understanding how the overall system behaves around them and how to present results in a way that feels trustworthy.
Curious how others here think about this:
- how do you benchmark hybrid systems like this?
- what kind of metrics actually matter in practice?
- and how do you avoid benchmarks that look good but don’t reflect real-world reliability?
- also i just wanted to know people opinion of the system i am suggestion on the basis of this small description , do u think it can e a good one ? if properly thought on as a actual usable library in real time project?
Not looking for a single answer,just interested in how people approach this in real systems.
Henry_old@reddit
benchmarks look okay but remote apis kill lat for threat detection i use local redis sqlite wal for real time sub ms response speed is alpha
Emergency-Rough-6372@reddit (OP)
i didnt quiet get what you are trying to convwy can you go a bit into details
Henry_old@reddit
remote apis mean net lag hft needs sub ms so keep data local redis or sqlite wal python fine net is bottleneck keep on same node
snugar_i@reddit
where did OP say anything about HFT?
Henry_old@reddit
dont need say hft to care bout speed slow code is slow code regardless if you like lag keep it remote i prefer sub ms infra for everything
snugar_i@reddit
then why Python? it's not exactly "blazingly fast"
Henry_old@reddit
python speed is fine if io is bottleneck net lag is 100ms python overhead is 1ms who cares about cpp if your ping is trash i ship faster in python use redis for state stay fast where it matters
snugar_i@reddit
You're a troll, but I'll respond anyway because why not - you prefer "sub ms" but "who cares about 1 ms"? Do you even know what the things you're saying mean? Also, learn to use punctuation, your stream of words is really hard to decipher
Henry_old@reddit
call it trolling i call it optimization 100ms net lag + 1ms python is bad enough if you think lag is okay you arent trading real volume stay slow stay poor
Emergency-Rough-6372@reddit (OP)
Yes, exactly that’s why I’m avoiding remote APIs as much as possible. Network latency becomes the bottleneck, especially if you’re aiming for very low response times.
My library is designed to run almost entirely locally, with only one or two rare exceptions. The APIs I mentioned are not external services, but rather the general backend APIs of the website that my library integrates with it essentially acts as a middleware layer on top of them.
Because of that, I want to minimize any additional delay my system introduces. Since it sits in the request–response path, even small overheads will directly add to the backend latency.
So the goal is to keep everything local (e.g., using something lightweight like Redis or SQLite in WAL mode) and achieve near sub-millisecond overhead, or at least as close as practically possible.
and it cases where i have a bit more delay or for more added security i will allow flexibility for developer to choose if they want the delay for that route.
Henry_old@reddit
exactly net is the enemy keep everything on same bare metal skip cloud bloat keep it local
Henry_old@reddit
python speed is fine if io is bottleneck net lag is 100ms python overhead is 1ms who cares about cpp if your ping is trash i ship faster in python use redis for state stay fast where it matters
Python-ModTeam@reddit
Your post was removed for violating Rule #2. All posts must be directly related to the Python programming language. Posts pertaining to programming in general are not permitted. You may want to try posting in /r/programming instead.
ProtossLiving@reddit
At the end of the day, I assume a user is going to use your system to device if something is a threat or not, right? Maybe something also some unsure state in between. So I assume you're returning a score of some sort. I assume for the sake of the user, you're providing some guidance of what those scores mean, like 80%+ means likely threat, 50% or less means no threat, otherwise unsure. If that's the case, you'd want to at least know how well you're doing with returning a meaningful result (ie. above 80 OR below 50), how well you're doing with false positives, and how well you're doing with false negatives.
Emergency-Rough-6372@reddit (OP)
Yes, you guessed a good part of it that’s more or less how it’s going to work. What you’re saying is right, and that’s exactly the part I’m concerned about.
In theory, I’ve tried to plan for most of it, but I’d need to test it myself before letting others use it. So I’m looking for a way to properly evaluate and validate the system on my own first.
Particular-Plan1951@reddit
In hybrid security systems the evaluation usually becomes more about operational impact than pure model accuracy. Metrics like precision/recall on known threats are useful, but the real question is how the system behaves in production: how often it flags legitimate traffic, how consistently it detects suspicious patterns, and whether the risk scores correlate with actual security incidents. I’ve seen teams build benchmark datasets from historical logs and replay them to measure both deterministic and probabilistic behavior together.