--- title: Engineering a Real-Time ML Network IDS Dashboard | daily.dev description: A walkthrough of building Sentinel, a real-time network intrusion detection dashboard that bridges the gap between a trained ML model and live traffic... canonical: https://daily.dev/posts/engineering-a-real-time-ml-network-ids-dashboard-68ubxa6mz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Engineering a Real-Time ML Network IDS Dashboard | daily.dev og:description: A walkthrough of building Sentinel, a real-time network intrusion detection dashboard that bridges the gap between a trained ML model and live traffic... og:url: https://daily.dev/posts/engineering-a-real-time-ml-network-ids-dashboard-68ubxa6mz og:image: https://api.daily.dev/og/posts/68ubxa6mZ.png og:image:alt: Engineering a Real-Time ML Network IDS Dashboard og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Engineering a Real-Time ML Network IDS Dashboard **[Medium](https://daily.dev/sources/medium_js)** · 8 min read · 0 upvotes · 1 comments ## Summary A walkthrough of building Sentinel, a real-time network intrusion detection dashboard that bridges the gap between a trained ML model and live traffic analysis. The system uses a Random Forest classifier trained on NSL-KDD, captures live packets with Scapy, reconstructs connection-level features using a rolling window, and displays verdicts in a Flask UI. Key engineering challenges covered include feature reconstruction from raw packets, avoiding train-serve mismatch by persisting model artifacts with joblib, and honestly handling features that cannot be fully derived from single packets. Validated against a Kali Linux VM using ping tests and SYN flood probes, achieving 99.68% accuracy on the held-out validation set while clearly acknowledging the lab's limitations versus production use. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Why is it hard to turn an NSL-KDD trained intrusion detection model into a live packet classifier? Because many NSL-KDD features like count, srv_count, same_srv_rate, and diff_srv_rate depend on recent connection history rather than a single packet, so they cannot be read directly from live traffic. A practical approach is to maintain a rolling in-memory window of recent packets and derive counts and service ratios from it, approximating rather than perfectly recreating the original dataset logic. _daily.dev surfaces engineering writeups like this for developers building real-time ML security pipelines._ ### How accurate is a Random Forest classifier trained on the NSL-KDD dataset for intrusion detection? A Random Forest model trained on NSL-KDD with a 70/30 stratified train-validation split reached 99.68 percent accuracy, with attack precision of 1.00, recall of 0.99, and F1 score of 1.00. These numbers reflect strong performance on the held-out validation split but do not guarantee similar results against modern or unseen adversarial traffic. _track real-world benchmark caveats like this before trusting ML security accuracy claims, via daily.dev._ ### Why is Random Forest a good choice for network intrusion detection models over other classifiers? Random Forest works well because it requires no feature scaling, handles mixed categorical and numeric inputs cleanly, and tolerates approximated live features that cannot be perfectly reconstructed from a single packet. These properties make it more forgiving than alternatives when moving from a clean training dataset like NSL-KDD to noisy live traffic. _developers weighing classifier choices for security tooling can follow comparisons like this on daily.dev._ ## Community discussion Top comments from developers on daily.dev. **@trevorsuna** · 0 upvotes > The honest bit about train-serve mismatch and missing packet-level features matters more than the 99.68% number. A lab IDS is a great learning tool, but production traffic will find every shortcut pretty quickly. ## Similar posts on daily.dev - [How I Built DeFi-Sentinel: Real-time Market Anomaly Monitoring and Battling RAM Issues](https://daily.dev/posts/how-i-built-defi-sentinel-real-time-market-anomaly-monitoring-and-battling-ram-issues-xjjpf0zph) · Coins Bench · 0 upvotes · 0 comments - [When the Sensor Starts Thinking: SnortML, Agentic AI, and the Evolving Architecture of Intrusion Detection](https://daily.dev/posts/when-the-sensor-starts-thinking-snortml-agentic-ai-and-the-evolving-architecture-of-intrusion-det-6llhu3ih9) · Stack Overflow Blog · 4 upvotes · 0 comments - [Medium](https://daily.dev/posts/medium-yf06gm83d) · Medium · 0 upvotes · 0 comments - [How to Create a Spring Boot Fraud Scoring Service](https://daily.dev/posts/how-to-create-a-spring-boot-fraud-scoring-service-kbhmhzaxf) · Foojay.io · 0 upvotes · 0 comments - [Transform and route security logs to Microsoft Sentinel tables using Observability Pipelines](https://daily.dev/posts/transform-and-route-security-logs-to-microsoft-sentinel-tables-using-observability-pipelines-xuiatl7yi) · Datadog · 0 upvotes · 0 comments --- Tags: [#security](https://daily.dev/tags/security), [#machine-learning](https://daily.dev/tags/machine-learning), [#flask](https://daily.dev/tags/flask), [#random-forest](https://daily.dev/tags/random-forest) [View this post on daily.dev](https://daily.dev/posts/engineering-a-real-time-ml-network-ids-dashboard-68ubxa6mz) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Engineering a Real-Time ML Network IDS Dashboard","url":"https://daily.dev/posts/engineering-a-real-time-ml-network-ids-dashboard-68ubxa6mz","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/engineering-a-real-time-ml-network-ids-dashboard-68ubxa6mz"},"datePublished":"2026-07-21T23:48:49.210Z","dateModified":"2026-09-14T06:37:27.272Z","description":"A walkthrough of building Sentinel, a real-time network intrusion detection dashboard that bridges the gap between a trained ML model and live traffic...","image":"https://media.daily.dev/image/upload/s--VDukGCjf--/f_auto/v1722860399/public/Placeholder%2002","thumbnailUrl":"https://media.daily.dev/image/upload/s--VDukGCjf--/f_auto/v1722860399/public/Placeholder%2002","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":1,"discussionUrl":"https://daily.dev/posts/engineering-a-real-time-ml-network-ids-dashboard-68ubxa6mz","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":1}],"keywords":"security,machine-learning,flask,random-forest","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"Engineering a Real-Time ML Network IDS Dashboard"}]} {"@context":"https://schema.org","@type":"WebPage","@id":"https://daily.dev/posts/engineering-a-real-time-ml-network-ids-dashboard-68ubxa6mz","comment":[{"@type":"Comment","text":"The honest bit about train-serve mismatch and missing packet-level features matters more than the 99.68% number. 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