# Intrusion Detection in IoT Networks Using Hyperdimensional Computing: A Case Study on the NSL-KDD Dataset

arXiv: https://arxiv.org/abs/2503.03037 · HTML: https://mlsanity.com/paper/2503.03037/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 2 of 2)

- [Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification](https://mlsanity.com/paper/2408.05347/) (2024)
- [Exploring Emerging Trends in 5G Malicious Traffic Analysis and Incremental Learning Intrusion Detection Strategies](https://mlsanity.com/paper/2402.14353/) (2024)

## Built on by (most cited first, 1 of 1)

- [D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection](https://mlsanity.com/paper/2606.13754/) (2026)

## Abstract (arXiv)

The rapid expansion of Internet of Things (IoT) networks has introduced new security challenges, necessitating efficient and reliable methods for intrusion detection. In this study, a detection framework based on hyperdimensional computing (HDC) is proposed to identify and classify network intrusions using the NSL-KDD dataset, a standard benchmark for intrusion detection systems. By leveraging the capabilities of HDC, including high-dimensional representation and efficient computation, the proposed approach effectively distinguishes various attack categories such as DoS, probe, R2L, and U2R, while accurately identifying normal traffic patterns. Comprehensive evaluations demonstrate that the proposed method achieves an accuracy of 99.54%, significantly outperforming conventional intrusion detection techniques, making it a promising solution for IoT network security. This work emphasizes the critical role of robust and precise intrusion detection in safeguarding IoT systems against evolving cyber threats.