Built a Network Traffic Classifier with Random Forest (96.8% Accuracy)
Gulrez Qayyum has developed a network traffic classifier using a Random Forest model, achieving an accuracy of 96.8%. The classifier is capable of identifying various types of network attacks and normal traffic. Qayyum shared insights on the project, including dataset preprocessing and real-world deployment considerations.
- ▪The classifier can detect DoS attacks, probe/reconnaissance traffic, R2L brute-force attempts, U2R privilege escalation, and normal traffic.
- ▪The project utilized the NSL-KDD dataset and achieved a production-ready model with less than 1ms inference time.
- ▪Qayyum provided a detailed article covering aspects such as feature selection, model training, and API integration.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/gulrez/built-a-network-traffic-classifier-with-random-forest-968-accuracy-52ai |
| Publication time | Sun, 17 May 2026 18:47:18 +0000 |
| Retrieval time | 2026-05-17T19:03:20.885Z |
| Last seen | 2026-05-17T19:03:20.885Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
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| 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 | wIdZcT4ZUdQ1 |
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| 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.
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 === 3896088) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Gulrez Qayyum Posted on May 17 Built a Network Traffic Classifier with Random Forest (96.8% Accuracy) #cybersecurity #ai #machinelearning #python I recently completed a cybersecurity + machine learning project where I trained a Random Forest model to classify network traffic into multiple attack categories using the NSL-KDD dataset.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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