用高维计算提升物联网网络异常检测，准确率达91.55%
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD
- 采用高维计算技术处理海量复杂数据
- 在KDDTrain+子集上达91.55%准确率
- 适合需要高效智能防护的物联网安全场景
随着物联网设备的快速增长，保障网络安全成为关键挑战。传统入侵检测系统（IDS）在高维复杂数据环境中难以有效识别复杂攻击。本文提出一种基于高维计算（HDC）的新型网络异常检测方法，应用于NSL-KDD数据集。该方法利用HDC在大规模数据处理中的高效性，可识别已知与未知攻击模式。模型在KDDTrain+子集上达到91.55%的准确率，优于传统方法。对比评估表明其性能优越，具备提升物联网网络安全检测能力的潜力，有助于构建更智能、更安全的网络防御体系。
原文摘要 · Abstract (English)
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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