Metasurfaces enable controllable manipulation of electromagnetic waves and have been shown to be valuable for wireless communications in many diverse ways. In this paper, we explore the notion that these useful components could also provide opportunities for a malicious agent. In particular, we define and experimentally demonstrate for the first time a “MetaSurface-in-the-Middle” (MSITM) attack. In this attack, the adversary Eve places a metasurface in the path of a directive transmission between Alice and Bob and targets to re-direct a portion of the signal towards herself, without being detected. Specifically, we show how Eve can design a metasurface that induces abrupt phase changes at the interface of the metasurface to controllably diffract directional links and establish furtive eavesdropping links. We explore the theoretical foundations of the MSITM attack and demonstrate that an effective metasurface can be prototyped in under 5 min at a minimal cost. We experimentally demonstrate the attack in a THz time-domain system and perform a set of over-the-air experiments. Our results indicate that the MSITM attack yields an acute vulnerability that can significantly reduce empirical secrecy capacity while leaving a minimal energy footprint, making the attack challenging to detect.

Using UAVs has recently emerged as a cost-efficient solution to assist first responders in search and rescue missions. Victims are usually equipped with wireless devices, which makes RF sensing an efficient solution for their localization in disaster situations. Nevertheless, the success of existing methods is highly diminished by the noisy nature of RF measurements. While leveraging recent advancements in lightweight gas sensing, we present in this ongoing work paper a novel localization approach that efficiently combines RF measurements with victim odor information while accounting for the dynamic nature of measurements' quality. We discuss the approach design, early simulation results, and ongoing experimental evaluation.

Wireless eavesdropping on phone conversations has become a major security and safety concern, especially with advancements toward 5G and beyond featuring higher frequencies and higher sensing resolution. As demonstrated recently, attackers can remotely detect even micron-scale acoustic vibrations emanating from a smartphone's earpiece via off-the-shelf millimeter-wave radar for audio information eavesdropping, all without the victim ever noticing. Here, we present a new architecture, MiSINFO, that not only thwarts such attacks but also enables the victim to counter-attack by spoofing of eavesdroppers with audio misinformation. With emerging attacks targeting the physical medium, i.e., acoustic signals, which cannot be protected by digital encryption and are the weakest segment of the communication chain, MiSINFO aims to systematically modify the eavesdroppers' fundamental sensing observations, concealing native signals while encoding alternate synthetic data. MiSINFO incorporates a low-profile, reconfigurable metasurface and double-inference principles to dynamically generate artificial audio-vibration signatures, injecting deceptive misinformation. We design, implement, and experimentally evaluate MiSINFO. Our results reveal that eavesdroppers detect none of the original words emitted by the speaker, while the injected misinformation is reconstructed with a low average word error rate of 2.29%. Our work represents the first such eavesdropping countermeasure which not only prevents attackers from accurately decoding the true signal but also uses a false signal to fool them into believing that they have succeeded. This approach transforms defensive measures from merely reactive to proactively deceptive, giving the defender an advantage and the capability to delude attackers into trusting false information.

Wireless eavesdropping on phone conversations has become a major security concern as attackers repurpose advanced wireless capabilities in 5G and beyond featuring higher frequencies and higher sensing resolution. Recent studies have demonstrated that attackers can exploit off-the-shelf millimeter-wave radars to covertly detect even micron-scale vibrations of smartphones caused by the earpiece during the phone conversation, eavesdropping on audio information without the victim ever noticing. In our IEEE S&P'25 paper, we present a new architecture that not only thwarts such attacks but also injects false signatures to fool eavesdroppers into believing they have succeeded. Here, we demonstrate the eavesdropping countermeasure technique that enables the user to hide his private acoustic signals and simultaneously inject an alternative signal via a low-profile, reconfigurable metasurface. We present a metasurface-based audio encoding method that generates artificial audio-vibration signatures to send deceptive audio information to eavesdroppers. We showcase experimental audio samples from both the attack and the proposed countermeasure, which transforms defensive strategies from merely reactive to proactively deceptive.

In this paper, we propose FloatSense, a novel balloon-based UAV network system for efficient and robust air pollution monitoring. Unlike prior related work commonly leveraging rotary-wing drones, FloatSense UAVs mainly exploit helium balloons to maintain elevation and use small lightweight normally-off fans as a propulsion mechanism. The proposed design enables as a result extended environmental sensing missions by staying afloat for weeks. However, the wind-dependent mobility nature of balloon systems involves multiple challenges in terms of system design and pollution mapping. We address in this paper the aforementioned challenges as we design and experimentally evaluate FloatSense in order to identify the benefits of the helium-powered flight mechanism on the accuracy of air pollution mapping compared to traditional rotatory-wing drones. We reveal that although balloon-based UAVs are prone to drifting off due to external forces like wind, FloatSense outperforms traditional drones even in the presence of considerable wind speeds. Moreover, we show that the wind-dependent balloon mobility nature also contributes to the performance improvement of FloatSense in air pollution monitoring missions.

Wireless backhaul links, already ubiquitous and expanding further with 5G and beyond, are employed for many critical functions, such as financial trading on Wall Street. In this work, we demonstrate for the first time that such links are acutely vulnerable to a new class of aerial metasurface attacks. In particular, we show how an adversary Eve designs and employs MetaFly to covertly manipulate the electromagnetic wavefront of the signals and remotely eavesdrop on highly directional backhaul links. Exploring the foundation of the attack, we demonstrate Eve’s strategy for generating eavesdropping diffraction beams by inducing pre-defined phase profiles at the aerial metasurface interface. We also show how Eve’s flight navigation approach can dynamically shape radiation patterns based on drone mobility via a wavefront-tailored flight refinement principle. We prototype MetaFly and demonstrate Eve’s lightweight, low-cost, transmissive, and power-free aerial metasurface. We implement the attack and perform a suite of over-the-air experiments in both a large indoor atrium and outdoor rooftops in a large metropolitan area. The results reveal that armed with MetaFly, Eve can intercept backhaul transmissions with nearly zero bit error rate while maintaining minimal impact on legitimate communication.

Metasurfaces enable controllable manipulation of electromagnetic waves and have been shown to improve wireless communications in many diverse ways. In this paper, we define and experimentally demonstrate for the first time a "MetaSurface-in-the-Middle'' (MSITM) attack. In this attack, the adversary Eve places a metasurface in the path of a directive transmission between Alice and Bob and targets to re-direct a portion of the signal towards herself, without being detected. In particular, we show how Eve can design a metasurface that induces abrupt phase changes at the interface of the metasurface to controllably diffract directional links and establish furtive eavesdropping links. We explore the theoretical foundations of the MSITM attack and demonstrate that an effective metasurface can be prototyped in under 5 min at the cost of several cents. We experimentally demonstrate the attack in a THz time-domain system and perform a set of over-the-air experiments. Our results indicate that the MSITM attack yields an acute vulnerability that can significantly reduce empirical secrecy capacity while leaving a minimal energy footprint, making the attack challenging to detect.

Metasurfaces enable controllable manipulation of electromagnetic waves and have been shown to improve wireless communications in many diverse ways. Investigating adversarial metasurfaces, we define and experimentally demonstrate for the first time a "MetaSurface-in-the-Middle'' (MSITM) attack in our paper \citeshaikhanov2022MSITM. In the attack, the adversary Eve places a metasurface in the path of a directive transmission between Alice and Bob and targets to re-direct a portion of the signal towards herself, without being detected. Here, we demonstrate the rapid fabrication of the MSITM employing only standard office supplies such as a printer, paper, foil, and laminator. We show that an effective metasurface can be prototyped in under $5$ min at the cost of several cents. We also demo the attack implementation in the THz network, presenting a video of the MSITM attacker establishing a diffractive eavesdropping link while maintaining the legitimate Alice-Bob link. Our results indicate that the attack yields an acute eavesdropping vulnerability while leaving a minimal energy footprint, making the attack challenging to detect.

We present FALCON, a novel autonomous drone network system for sensing, localizing, and approaching RF targets/sources such as smartphone devices. Potential applications of our system include disaster relief missions in which networked drones sense the Wi-Fi signal emitted from a victim’s smartphone and dynamically navigate to accurately localize and quickly approach the victim, for instance, to deliver the time-critical first-aid kits. For that we exploit Wi-Fi’s recent fine time measurement (FTM) protocol to realize the first on-drone FTM sensor network that enables accurate and dynamic ranging of targets in a mission. We propose a flight planning strategy that adapts the trajectory of the drones to concurrently favor localizing and approaching the target. Namely, our approach jointly optimizes the drones’ diversity of observations and the target approaching process, while flexibly trading off the intensities of the potentially conflicting objectives. We implement FALCON via a custom-designed multidrone platform and demonstrate up to$2\times $localization accuracy compared to a baseline flocking approach, while spending 30% less time localizing targets.

We present the design, implementation, and experimental evaluation of ASTRO, a modular end-to-end system for distributed sensing missions with autonomous networked drones. We introduce the fundamental system architecture features that enable agnostic sensing missions on top of the ASTRO drones. We demonstrate the key principles of ASTRO by using on-board software-defined radios to find and track a mobile radio target. We show how simple distributed on-board machine learning methods can be used to find and track a mobile target, even if all drones lose contact with a ground control. Also, we show that ASTRO is able to find the target even if it is hiding under a three-ton concrete slab, representing a highly irregular propagation environment. Our findings reveal that, despite no prior training and noisy sensory measurements, ASTRO drones are able to learn the propagation environment in the scale of seconds and localize a target with a mean accuracy of 8 m. Moreover, ASTRO drones are able to track the target with relatively constant error over time, even as it moves at a speed close to the maximum drone speed.