用变分自编码器潜空间提升入侵检测置信度。
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
- 基于潜空间构建异常检测置信度指标
- 重建误差与置信度相关性达0.45
- 适合关注安全系统可信性的研究者
本文提出一种基于变分自编码器（VAE）架构的新方法，通过潜空间表示构建置信度度量，以增强入侵检测系统（IDS）在异常检测中的可靠性。针对NSL-KDD数据集上的二分类任务，该方法能有效区分正常与恶意网络行为。实验表明，重建误差与所提置信度指标间存在0.45的显著相关性，验证了该方法在提升网络安全部署中异常检测准确性和可信度方面的潜力。
原文摘要 · Abstract (English)
This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security.
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