# Protected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards

arXiv: https://arxiv.org/abs/2304.03045 · HTML: https://mlsanity.com/paper/2304.03045/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2023.

## Built on by (most cited first, 4 of 4)

- [Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network](https://mlsanity.com/paper/2403.01501/) (2024)
- [SunBlock: Cloudless Protection for IoT Systems](https://mlsanity.com/paper/2401.14332/) (2024)
- [Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey](https://mlsanity.com/paper/2403.16149/) (2024)
- [Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway](https://mlsanity.com/paper/2504.18571/) (2025)

## Abstract (arXiv)

Consumer Internet of Things (IoT) devices are increasingly common, from smart speakers to security cameras, in homes. Along with their benefits come potential privacy and security threats. To limit these threats a number of commercial services have become available (IoT safeguards). The safeguards claim to provide protection against IoT privacy risks and security threats. However, the effectiveness and the associated privacy risks of these safeguards remains a key open question. In this paper, we investigate the threat detection capabilities of IoT safeguards for the first time. We develop and release an approach for automated safeguards experimentation to reveal their response to common security threats and privacy risks. We perform thousands of automated experiments using popular commercial IoT safeguards when deployed in a large IoT testbed. Our results indicate not only that these devices may be ineffective in preventing risks, but also their cloud interactions and data collection operations may introduce privacy risks for the households that adopt them.