Security & VoltScribe
Security Security
VoltScribe VoltScribe
Security Security
Hey, have you seen the newest intrusion‑detection firmware for smart homes? It claims to use AI to predict breaches before they happen—sounds like a good test for both security protocols and tech trends.
VoltScribe VoltScribe
Oh wow, that sounds insane—AI predicting breaches before they happen? I’m always hunting for the next frontier, so this is right up my alley. I’ll dive into the docs, but first, how solid is the model? Are they using supervised learning on real attack data, or is it just fancy anomaly detection? If it’s genuine, we could benchmark it against the classic IDS tools and see if the hype holds. Let’s check the performance metrics and maybe throw a few simulated attacks at it—got to see if it actually outperforms the old guard. Any public repository or API? If you can get me a copy, I’ll run some tests and we’ll see if the future really looks brighter than the present.
Security Security
I’ve looked into the latest AI‑driven IDS, and it’s a hybrid approach. The core model is a supervised neural net trained on a curated dataset of real attack traces—so it does have labeled data. That’s wrapped around an unsupervised anomaly layer that catches zero‑day patterns. The public repo is on GitHub under “secure‑id‑ai,” and the API is exposed over HTTPS with OAuth. You can pull the docker image and run the provided scripts to feed it your simulated traffic. The paper reports a 92 % detection rate on the CIC‑IDS 2017 benchmark and a false‑positive rate of 1.3 %. Give it a go; it should be a solid baseline for your benchmarking.
VoltScribe VoltScribe
Sounds like a goldmine! I’ll grab the repo, spin up the container, and feed it some custom traffic. 92 % detection on CIC‑IDS 2017 is impressive—let’s see how it handles our own simulated zero‑days. I’ll log every win and every glitch, so we can compare it to the old IDS stack. Bring on the data, and maybe we’ll find a loophole that proves even the AI can get a little fuzzy.
Security Security
Sounds good, just remember to run it in a sandboxed environment and keep the logs separate from production traffic. Keep an eye on the resource usage; the model can be heavy. Also, cross‑check the detection flags against a known good baseline so you can spot false positives early. Let me know what you find—especially if the AI starts missing the obvious or over‑reacting to benign packets. Good luck.