利用物理存在感应来保护语音助理系统的安全.
Bang Tran1, Shenhui Pan1, Xiaohui Liang1
1Department of Computer Science, University of Massachusetts Boston.
IEEE International Conference on Communications : [proceedings]. IEEE International Conference on Communications
|September 8, 2025
概括
本研究介绍了语音助理系统 (VAS) 的防御系统,以检测和防止恶意语音重播和注入攻击. 该系统使用Mel-Cepstral频率系数 (MFCC) 和深度学习,在智能家居场景中实现76.4%-89.1%的检测精度.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 智能家居技术 智能家居技术
背景情况:
- 语音助理系统 (VAS) 提供便利性,但易受语音重播和注入攻击等安全威胁的影响.
- 攻击者可以利用附近的受损扬声器远程操纵智能家居中的VAS设备.
- 现有的防御可能需要额外的硬件或用户努力,限制实际应用.
研究的目的:
- 为VAS提供一个新的防御系统,直接集成到设备中.
- 在没有额外的硬件或用户干预的情况下,保护VAS免受语音重播和注入攻击.
- 为了利用语音和无线数据之间的相关性进行攻击检测.
主要方法:
- 从VAS设备连续收集语音和无线数据.
- 从两个数据流中提取Mel-Cepstral频率系数 (MFCC) 的特征.
- 在时间序列数据上训练的深度学习模型的应用,以区分用户命令和恶意输入.
主要成果:
- 拟议的系统在现实智能家居环境中成功检测了语音重播和注入攻击.
- 在各种测试场景中,检测概率从76.4%到89.1%不等.
- 该系统有效地利用了由用户活动影响的语音和无线数据之间的相关性.
结论:
- 开发的防御系统为增强VAS安全提供了强大的,综合的解决方案.
- 该方法有效地减轻了智能家居环境中复杂的基于语音的攻击.
- 这种方法为语音助理设备提供了用户友好且独立于硬件的安全增强.
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