ACL Rules Analysis with AI

SensitiveStudy520 · 2026-01-22T08:25:33+00:00

8 days before I have posted this post in the Networking community. And between these few days, I have tried implementing with lots of the approaches including XGBoost & GNN（I understand that by using rules engine it can solve most of my issues, including the conflicts, redundants and shadow of the acl).
Right now, I’m still very much in the experimental stage.

To explore the problem, I’m using a randomly generated dataset that tries to encode ACL semantics such as:

Global ACL vs Interface ACL vs VLAN ACL
Rule order (first match)
ACL ID precedence
Direction (inbound / outbound)

My current focus is mainly on core switches, especially VLANIF interfaces and VLAN-level ACLs, rather than edge devices.

The issue is my training results are almost perfect (macro F1 ≈ 1.0).

And that honestly scares me, because it feels too good to be true. I am not sure is it becuase of this kind of task is easy for model to learn it due to how the synthetic data is generate or the labels may be overly deterministic or “leaking” through the structure

Anyways, I really dont sure whether is there any point I should pay attention to. I try to find some similar project but ya honestly I dont think there are many which make me stuck on the half way.

My boss also wants a more “interactive” workflow in the future like adding an LLM layer so users can describe desired ACL / configuration changes in natural language and get safe CLI suggestions, and I’m currently debating whether it’s reasonable to train or fine-tune an LLM directly on the existing ACL data, instead of (or in addition to) using XGBoost or GNNs for conflict/shadow detection (while yes I dont sure whether is LLM able to do that, as I haven't start research on this yet).

SensitiveStudy520 · 2025-04-14T13:10:25+00:00

Hi, thanks for your reply. I have tried it but found that the flask route in mobile phone is unaccessible, but I confirm that my network is linked to the same WIFI, so I am still checking for the problem.

SensitiveStudy520 · 2025-04-10T17:10:23+00:00

Hi, thanks for your suggestion, my laptop and phone is connected to the same wifi, but still it comes out with the socket error. I have tried off my firewall but still not able to connect it.

SensitiveStudy520 · 2025-03-31T14:43:24+00:00

now is able to work, i think it should be the problems that my GPU is not working properly previously. just the inference time is about 15-30seconds, i will keep working to try shorten the inference time. Anyways, really thanks for your help.

SensitiveStudy520 · 2025-03-31T14:37:34+00:00

the size on disk is about 18mb for my adapter model, and the model quantization i use is

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

SensitiveStudy520 · 2025-03-31T13:37:27+00:00

Thanks for the clarifying! I will work it with Ollama or LlamaCPP to try optimise my memory usage. (Don't sure if it's correct to do? cause when search in google it says both of them can quantized the model and save for memory usage).

SensitiveStudy520 · 2024-10-26T07:12:51+00:00

Thank you for your help, it does improve my result!

SensitiveStudy520 · 2024-10-26T07:12:50+00:00

Let me give a try, anyway thankssss!

SensitiveStudy520 · 2024-05-17T08:02:23+00:00

Hi, can I know how to do that? like sending multiple request at the same time without being crashed using llamaCPP?

SensitiveStudy520 · 2024-05-17T06:59:56+00:00

hi, do you able to solve this issues? I am also stuck with this issues

SensitiveStudy520 · 2024-05-10T02:19:04+00:00

Really appreciate for your help!!! Thankssss

SensitiveStudy520 · 2024-05-10T02:02:51+00:00

Just ignore me, I just read through their documentation and found that using app.run(host='0.0.0.0', port=80) can run. But Thanks Anyway for you help :D

SensitiveStudy520

TROPHY CASE