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

2025-03-04

AI Summary

Traditional intrusion detection systems (IDS) for IoT exhibit poor generalization on high-dimensional, complex network traffic and heavily rely on prior knowledge of known attacks. Method: This paper pioneers the application of Hyperdimensional Computing (HDC) to anomaly detection on the NSL-KDD dataset, proposing a lightweight HDC-based framework that integrates binary symbolic encoding, feature binding, and similarity matching—enabling efficient detection of both known and unknown attacks without training any classifier. Results: Evaluated on the KDDTrain+ subset, the method achieves 91.55% accuracy, significantly outperforming classical machine learning and deep learning baselines. By eliminating reliance on labeled attack samples, it enhances model robustness and generalization while maintaining computational efficiency. This work establishes a novel unsupervised/low-supervision detection paradigm tailored for resource-constrained IoT environments.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• Detecting network anomalies in IoT using hyperdimensional computing.
• Overcoming limitations of traditional IDS in complex data environments.
• Enhancing IoT network security with advanced anomaly detection techniques.

Innovation

• Hyperdimensional computing for IoT anomaly detection
• Efficient processing of large-scale NSL-KDD dataset
• Achieved 91.55% accuracy, outperforming traditional methods