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

• URL: http://arxiv.org/abs/2503.03031v1
• Date: Tue, 04 Mar 2025 22:19:26 GMT
• Title: Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD
• Authors: Ghazal Ghajari, Ashutosh Ghimire, Elaheh Ghajari, Fathi Amsaad,
• Abstract summary: This paper presents a novel approach to network anomaly detection using hyperdimensional computing (HDC) techniques.<n>The proposed method leverages the efficiency of HDC in processing large-scale data to identify both known and unknown attack patterns.<n>The model achieved an accuracy of 91.55% on the KDDTrain+ subset, outperforming traditional approaches.
• Score: 0.2399911126932527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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