# Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD

arXiv: https://arxiv.org/abs/2503.03031 · HTML: https://mlsanity.com/paper/2503.03031/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [Anomaly Detection-Based Unknown Face Presentation Attack Detection](https://mlsanity.com/paper/2007.05856/) (2020)
- [Positional Encoding in Transformer-Based Time Series Models: A Survey](https://mlsanity.com/paper/2502.12370/) (2025)
- [Hybrid Efficient Unsupervised Anomaly Detection for Early Pandemic Case Identification](https://mlsanity.com/paper/2408.05347/) (2024)
- [Machine Learning Models for Reinforced Concrete Pipes Condition Prediction: The State-of-the-Art Using Artificial Neural Networks and Multiple Linear Regression in a Wisconsin Case Study](https://mlsanity.com/paper/2502.00363/) (2025)

## Built on by (most cited first, 3 of 3)

- [Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control](https://mlsanity.com/paper/2505.03134/) (2025)
- [Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels](https://mlsanity.com/paper/2508.20193/) (2025)
- [D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection](https://mlsanity.com/paper/2606.13754/) (2026)

## Abstract (arXiv)

With the rapid growth of IoT devices, ensuring robust network security has become a critical challenge. Traditional intrusion detection systems (IDSs) often face limitations in detecting sophisticated attacks within high-dimensional and complex data environments. This paper presents a novel approach to network anomaly detection using hyperdimensional computing (HDC) techniques, specifically applied to the NSL-KDD dataset. The proposed method leverages the efficiency of HDC in processing large-scale data to identify both known and unknown attack patterns. The model achieved an accuracy of 91.55% on the KDDTrain+ subset, outperforming traditional approaches. These comparative evaluations underscore the model's superior performance, highlighting its potential in advancing anomaly detection for IoT networks and contributing to more secure and intelligent cybersecurity solutions.