A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
Abstract
:1. Introduction
- Complete picture and novel taxonomy: We present a complete picture of the literature in the area of secure WiFi sensing and propose a novel taxonomy for technologies for attack and defense of WiFi sensing, respectively, which guides the first step;
- Inclusive and up-to-date coverage: Our survey is inclusive, encompassing the latest advancements in secure WiFi sensing technologies. By integrating recent developments, we ensure that our work remains relevant and valuable to the research community;
- Comprehensive comparison and summary: We comprehensively compare and summarize existing technologies for attack and defense of WiFi sensing to provide readers with a thorough understanding of this area;
- Identification of key challenges and future directions: Our survey discusses the key challenges and future directions, which may inspire researchers and developers to carry on further research in WiFi sensing and build various applications to realize privacy-preserving, reliable, ubiquitous, and accurate WiFi sensing.
2. Background
2.1. Concept and Scope
2.2. WiFi Sensing Basics
2.3. Taxonomy
- Active attack. (1) Definition: Attacks that involve direct interaction with the WiFi sensing system to alter, disrupt, or damage the wireless channel or its operations. (2) Objective: The attacker aims to interfere with the normal functioning of the sensing system, causing it to produce inaccurate or unusable results;
- Passive attack. (1) Definition: Attacks that focus on monitoring or eavesdropping on the wireless channel or sensing results without altering them. (2) Objective: The attacker aims to gather sensitive information or infer private data without being detected.
- Active defense. (1) Definition: Protective measures taken directly by the sensing target (e.g., a user, device, or system) to safeguard against attacks. (2) Objective: The target actively engages in its own protection to mitigate potential threats;
- Passive defense. (1) Definition: Protective measures adopted by external parties, such as users of the sensing data or third-party entities, to secure the sensing information. (2) Objective: These measures protect the sensing data or system without requiring direct involvement from the sensing target.
3. Attacks Against WiFi Sensing
3.1. Active Attack
3.2. Passive Attack
3.3. Discussion
4. Defenses for WiFi Sensing
4.1. Active Defense
4.2. Passive Defense
4.3. Discussion
5. Challenges and Opportunities
5.1. Attackers
5.2. WiFi Sensing Algorithm Developers
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Category | Attack Aim | Sensing Scenario | Potential Defense |
---|---|---|---|---|
WiAdv [60] | Active attack | Introducing disturbance to the channel to decrease the sensing performance | Gesture recognition | Physical isolation Interference suppression |
Liu et al. [61] | Active attack | Introducing CSI absence to decrease the sensing performance | Gesture recognition | Physical isolation Interference suppression Anomaly detection |
Song et al. [62] | Active attack | Injecting false data to sensing datasets to decrease the sensing performance | Activity recognition | Adversarial training Anomaly detection |
Li et al. [51] | Active attack | Manipulating WiFi preambles to decrease the sensing performance | Activity recognition User authentication | Adversarial training Anomaly detection |
Liu et al. [63] | Active attack | Introducing disturbance to the channel to decrease the sensing performance | Location estimation | Adversarial training Anomaly detection |
WiCAM [64] | Active attack | Introducing adversarial noise in the received signals to decrease the sensing performance | Gesture recognition Activity recognition User authentication | Adversarial training Anomaly detection |
RIStealth [65] | Active attack | Rendering a moving individual undetectable by WiFi intrusion detection systems | Intrusion detection | Anomaly detection Device upgrade |
IS-WARS [54] | Active attack | Injecting cross-technology signals to the channel to decrease the sensing performance | Activity recognition | Signal processing Adversarial training |
Shi et al. [66] | Passive attack | Identifying users’ private information in sensing datasets, like height and weight | Gesture recognition Activity recognition | Data anonymization |
Hernandez et al. [55] | Passive attack | Tracking and counting the flow of traffic throughout a building | Occupancy monitoring Crowd counting | Physical isolation Channel disturbance |
ActListener [67] | Passive attack | Eavesdropping on user activities imperceptibly in any location of user sensing area | Activity recognition | Physical isolation Channel disturbance |
WiPeep [52] | Passive attack | Eavesdropping locations of indoor WiFi devices | Location estimation | Randomizing ToF Fake packets detection |
Method | Category | Defense Aim | Sensing Scenario | Targeted Attack |
---|---|---|---|---|
Zhou et al. [80] | Active defense | Misclassifying the private type of information while the others still are recognizable | Activity recognition | Information eavesdropping Behavioral snooping |
Wobly [81] | Active defense | Anonymizing users’ identities when performing accurate gait authentication | Gait recognition | Information eavesdropping Behavioral snooping |
IRShield [56] | Active defense | Obfuscating wireless channels to prevent overhearing of sensitive information | Motion detection | Information eavesdropping Behavioral snooping |
MIRAGE [57] | Active defense | Utilizing the downlink physical layer information to defense eavesdropper | Location estimation | Information eavesdropping Location snooping |
Zhao et al. [82] | Active defense | Creating plausible dummy locations to preserve indoor localization information | Location estimation | Information eavesdropping Location snooping |
SecureSense [83] | Active defense | Achieving consistent predictions of WiFi sensing systems regardless of input perturbations | Activity recognition | Adversarial attack |
Zhang et al. [84] | Active defense | Decreasing the recognition accuracy of the protected semantic significantly | User authentication Activity recognition | Information eavesdropping Identity snooping Activity snooping |
WiCloak [85] | Active defense | Rendering location information obtained by eavesdroppers meaningless | Location estimation | Information eavesdropping Location snooping |
PriFi [59] | Passive defense | Helping users to perceive ongoing observations from systems in the user’s current environment | \ | Information eavesdropping |
PriLa [86] | Passive defense | Facilitating location authentication without compromising user’s location privacy | Location estimation | Information eavesdropping Location snooping |
Wang et al. [87] | Passive defense | Protecting user’s positioning results in cloud-based indoor positioning systems | Location estimation | Information eavesdropping Location snooping |
Rusca et al. [88] | Passive defense | Performing crowd counting and tracking within large-scale gatherings | Crowd monitoring | Information eavesdropping Crowd flow snooping |
PrivateBus [58] | Passive defense | Protecting users from the leaked information in bus WiFi systems | Foot trace tracking Finger trace tracking | Information eavesdropping Trace snooping |
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Liu, X.; Meng, X.; Duan, H.; Hu, Z.; Wang, M. A Survey on Secure WiFi Sensing Technology: Attacks and Defenses. Sensors 2025, 25, 1913. https://doi.org/10.3390/s25061913
Liu X, Meng X, Duan H, Hu Z, Wang M. A Survey on Secure WiFi Sensing Technology: Attacks and Defenses. Sensors. 2025; 25(6):1913. https://doi.org/10.3390/s25061913
Chicago/Turabian StyleLiu, Xingyu, Xin Meng, Hancong Duan, Ze Hu, and Min Wang. 2025. "A Survey on Secure WiFi Sensing Technology: Attacks and Defenses" Sensors 25, no. 6: 1913. https://doi.org/10.3390/s25061913
APA StyleLiu, X., Meng, X., Duan, H., Hu, Z., & Wang, M. (2025). A Survey on Secure WiFi Sensing Technology: Attacks and Defenses. Sensors, 25(6), 1913. https://doi.org/10.3390/s25061913