How I Built an Adaptive "Immune System" for Cloud Traffic

Apr 29, 2026 · 8:18 AM UTC ·4 min read · 0 reactions · 0 comments · 33 views

#programming #devops #security #cloud #system design #Wandile Ndlovu #Nextcloud #Nginx #Docker #Python #Flask #Linux Kernel #iptables

TL;DR · WeSearch summary

Wandile Ndlovu developed an adaptive defense system for cloud traffic that learns normal behavior and detects anomalies in real time. The system uses statistical analysis, sliding windows, and dynamic baselines to identify suspicious activity on a Nextcloud instance. It automatically enforces bans via iptables and provides real-time observability through a custom UI.

Key facts

▪The system uses a time-based sliding window with Python's deque to track requests within the last 60 seconds.
▪It establishes a baseline of normal traffic by collecting 30 minutes of data and recalculating mean and standard deviation every minute.
▪Anomalies are detected using a Z-score greater than 3.0 or a 5x increase over baseline, with stricter thresholds for high error rates.
▪Banned IPs are managed through escalating iptables DROP rules and a backoff schedule ranging from 10 minutes to permanent bans.
▪A Flask-based UI provides real-time metrics including request rates, banned IPs, and system health.

About this source

DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.

All DEV.to (Top) coverage →

Original article

DEV.to (Top)

Read full at DEV.to (Top) →

Story provenance

Source · retrieval · rights · ranking — open for full record

inspect →

Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.

Record

Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/wandile_ndlovu_7dd22d4943/how-i-built-an-adaptive-immune-system-for-cloud-traffic-53b
Publication time	Wed, 29 Apr 2026 08:18:42 +0000
Retrieval time	2026-04-29T08:31:52.291Z
Last seen	2026-04-29T08:31:52.291Z
Headline source	Publisher (no WeSearch rewrite)
Excerpt source	publisher body
Excerpt method	First ~120 words (~800 chars) of extracted publisher body, fair-use limited.
Summary	WeSearch · cerebras-chat (WeSearch summarizer)
Summary source text	contentText
Citation coverage	Summary is a WeSearch-generated derivative; primary citation is the original publisher URL.
Cluster	9ViCac6qy5sw
Cluster logic	Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison.
Ranking reason	Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking.
Publisher visit	Yes — open original
Substitutes article?	No — link-out required for full text

Rights status (four layers)

Publisher-declared

No publisher-confirmed rights record for this source yet.

Machine-readable

No source-specific machine-readable restriction detected beyond the public feed.

WeSearch interpretation

WeSearch declared handling (basis: Derived from the published RSS/Atom feed). This is WeSearch policy, not a legal grant on the publisher's behalf.

Unknown

Retrieval and training permissions are not asserted unless the publisher confirms them.

WeSearch handling by dimension

Indexing	May the item be indexed (stored, ranked, made findable)?	Allowed
Snippet	May a short excerpt of the publisher's text be shown?	Allowed
AI summary	May WeSearch generate its own short summary of the article?	Limited
Retrieval / RAG	May the content be exposed for third-party retrieval-augmented generation?	Not asserted
Model training	May the content be used to train AI models?	Not asserted
Commercial reuse	May the content be reused commercially?	Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Open original publisher → Source rights JSON → Machine-readable provenance → Rights framework → WESEARCH-STORY-PROVENANCE-V1

Opening excerpt (first ~120 words) tap to expand

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3666948) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Wandile Ndlovu Posted on Apr 29 How I Built an Adaptive "Immune System" for Cloud Traffic #programming #devops #security #cloud Recently, I was tasked with a challenge: Build an automated defense system for a live Nextcloud instance. The goal wasn't just to block "bad guys," but to build a system that actually learns what a normal day looks like and reacts when things get weird.

…

Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).

Anonymous · no account needed

Discussion

0 comments