# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 5 Challenges and Opportunities
### Attackers  
**(1) Imperceptible Adversarial Attack**: Even though many existing attack techniques can effectively pose significant threats to WiFi sensing systems, they still have large vulnerabilities that could be detected or mitigated by defensive mechanisms. For attackers,one major challenge is to develop imperceptible adversarial attack techniques that can manipulate WiFi signals in real time without being noticed by the system or triggering alarms. This demands a deep understanding of signal processing, as WiFi sensing systems rely heavily on the fine-grained manipulation of CSI and RSSI. Attackers must also be well versed in wireless communication protocols to manipulate signal characteristics at a low level and also in adversarial machine learning, which can help in generating subtle perturbations or modifications to signals that evade detection. The opportunity for attackers lies in exploiting vulnerabilities in the algorithm's processing pipeline--especially in how these systems interpret and classify changes in the wireless environment. By understanding how these systems process data from different antennae, frequencies, and channels, attackers can develop techniques that introduce imperceptible alterations to the signal flow. These manipulations could lead to incorrect sensing results, such as generating ghost objects, altering object trajectories, or misidentifying activities, all without the system realizing that it has been compromised, which could raise the attack success rate. Ultimately, the development of such advanced techniques could expose critical security gaps in current WiFi sensing systems, paving the way for future advancements in defensive mechanisms. The study of these vulnerabilities could inspire algorithm developers to design more robust systems that incorporate adversarial training or real-time anomaly detection to defend against such sophisticated attacks. By identifying and addressing these weaknesses early, the field can move towards creating more secure and resilient WiFi sensing technologies. **(2) Advanced Attack against Defensive Means**: There are already many corresponding defense measures for common attack techniques targeting WiFi sensing systems, including encryption, signal obfuscation, jamming detection, and anomaly detection algorithms. These measures are designed to safeguard the integrity and accuracy of WiFi sensing, protecting against threats like spoofing, jamming, and adversarial attacks. However, attackers are constantly evolving their methods, striving to bypass these defenses and exploit system vulnerabilities. To deal with these existing security measures, attackers must develop more advanced, stealthy, and adaptive attack techniques that can evade detection and overcome countermeasures. For instance, traditional spoofing attacks involve injecting falsified data into the WiFi sensing system to generate incorrect outputs. However, these attacks often create inconsistencies that can be detected by anomaly detection mechanisms. To bypass these defenses, attackers can develop context-aware spoofing attacks, where they gather detailed environmental data in real time and use this information to craft more realistic and undetectable spoofing signals. If a WiFi sensing system is being used to monitor human movements in a room, an attacker could carefully observe the movements of individuals over time and adjust their spoofing attacks to match the expected patterns. By aligning the spoofed signals with real-world events, attackers can make it much harder for the system to distinguish between legitimate and falsified data. This kind of context-aware attack would require sophisticated sensing and machine learning techniques to model the environment and make dynamic adjustments to the spoofed signals. To further deal with defense measures, attackers can combine multiple attack vectors into coordinated multi-modal attacks, where different aspects of the WiFi sensing system are targeted simultaneously.