Stress and poor sleep remain pervasive challenges in modern life, yet traditional relaxation practices such as pranayama (breathing exercises) require guidance, discipline, and environments that are often difficult to sustain. VR–based relaxation tools have emerged as alternatives, but their abrupt immersion into fully virtual environments can feel disruptive and misaligned with the gradual nature of meditative practices. To address this gap, we collaborated with pranayama practitioners in a co-design process to develop Zenflow, an MR system that blends subtle visuals and breathing cues to gradually transform the user’s surroundings into a restorative virtual space. We evaluated the system in a 3 week within-subjects study (N=12), comparing traditional Pranayama with two variations of Zenflow. Results show that Zenflow transition design significantly improved self-reported sleep quality and objective measures of stress and sleep. Our work contributes design insights and evidence that gradual environmental transition can improve MR systems for stress management.

Unmanned Aerial Vehicles (UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence. Existing datasets are either limited in diversity or synthetic, lacking real acoustic interactions, and there are no standardized setups for drone audition. To this end, we present DroneAudioset (The dataset is publicly available at https://huggingface.co/datasets/ahlab-drone-project/DroneAudioSet/ under the MIT license), a comprehensive drone audition dataset featuring 23.5 hours of annotated recordings, covering a wide range of signal-to-noise ratios (SNRs) from -57.2 dB to -2.5 dB, across various drone types, throttles, microphone configurations as well as environments. The dataset enables development and systematic evaluation of noise suppression and classification methods for human-presence detection under challenging conditions, while also informing practical design considerations for drone audition systems, such as microphone placement trade-offs, and development of drone noise-aware audio processing. This dataset is an important step towards enabling design and deployment of drone-audition systems.

Drone delivery is envisioned to be the delivery mode of the future due to its capability to provide autonomous, end-to-end delivery. Such rapid growth of the drone market necessitates careful checks on drone flight delivery, as a failure in any of a drone’s parts can result in an overestimation of the drone’s battery life, an unexpected increase in delivery time, or even a drone crash. Prior works utilize onboard sensors to detect potential drone failures during flight, which is a reactive approach where the problem may have already occurred. In this work, we propose PADrone , a pre-flight and an automated drone abnormality detection system that leverages contactless radio frequency– (RF) based vibration sensing. PADrone utilizes an end-to-end deep learning pipeline to differentiate various abnormalities in motors, propellers, and other drone’s parts, by leveraging their unique vibration fingerprints . PADrone uses a frequency-modulated continuous wave radar-based RF system to capture these unique drone vibrations using an RF bandwidth of 150 MHz in the industrial, scientific, and medical band (5.8 GHz). Our real-world evaluations show that PADrone can classify various drone abnormalities with an average accuracy of 97.5%.

Microphones are an integral part of a wide range of devices owing to their utility in communication and voice-controlled assistance. However, the downside to microphones' ubiquity is the increase in eavesdropping that lead to inference attacks, such as recovering passwords by merely recording ambient sounds. To overcome such attacks, researchers have proposed several microphone detection and deterrence methods. However, existing methods have several disadvantages such as lacking generalizability and requiring hardware modifications. In this paper, I examine the microphone security space by taking two past works as examples. Specifically, I demonstrate an attack that enables recreation of physical keys to unlock doors from recordings of sound of key insertion into the keyhole. Subsequently, I propose an eavesdropping detection technique utilizing electromagnetic leakage signals from microphone hardware which is generalizable across devices without requiring hardware changes. Finally, I present several open problems and their challenges towards achieving microphone security.

The proliferation of Internet of Things (IoT) applications relying on Low Power Wide Area Networks (LPWANs) demands robust and energy-efficient communication solutions. Among various LP-WAN technologies, LoRa emerges as a prominent choice due to its long-range capabilities and low energy consumption. However, the practical deployment of LoRa is hindered by significant signal degradation caused by channel and hardware noise, especially in urban environments. We introduce GLoRiPHY, a novel generative framework designed to enhance the reception quality of LoRaPHY signals through a channel-aware denoising mechanism. Utilizing a transformer-based architecture, GLoRiPHY leverages the known preamble of LoRaPHY signals to compensate for channel-induced distortions, thereby generating a clean signal suitable for direct demodulation. The system integrates Convolutional Neural Networks (CNNs) for efficient feature encoding and decoding, maintaining a compact model footprint even at higher Spreading Factors (SFs). Evaluations on real-world and simulated datasets show that in comparison to the current state-of-the-art solution, GLoRiPHY significantly lowers the Symbol Error Rate (SER) by up to 2.85x and demonstrates generalizability in unseen environments, while reducing inference times by up to 5.75x.

Automotive Keyless Entry (RKE) systems provide car owners with a degree of convenience, allowing them to lock and unlock their car without using a mechanical key. Today’s RKE systems implement disposable rolling codes, making every key fob button press unique, effectively preventing simple replay attacks. However, a prior attack called RollJam was proven to break all rolling code–based systems in general. By a careful sequence of signal jamming, capturing, and replaying, an attacker can become aware of the subsequent valid unlock signal that has not been used yet. RollJam, however, requires continuous deployment indefinitely until it is exploited. Otherwise, the captured signals become invalid if the key fob is used again without RollJam in place. We introduce RollBack, a new replay-and-resynchronize attack against most of today’s RKE systems. In particular, we show that even though the one-time code becomes invalid in rolling code systems, replaying a few previously captured signals consecutively can trigger a rollback-like mechanism in the RKE system. Put differently, the rolling codes become resynchronized back to a previous code used in the past from where all subsequent yet already used signals work again. Moreover, the victim can still use the key fob without noticing any difference before and after the attack. Unlike RollJam, RollBack does not necessitate jamming at all. In fact, it requires signal capturing only once and can be exploited at any time in the future as many times as desired. This time-agnostic property is particularly attractive to attackers, especially in car-sharing/renting scenarios in which accessing the key fob is straightforward. However, while RollJam defeats virtually any rolling code–based system, vehicles might have additional anti-theft measures against malfunctioning key fobs, hence against RollBack. Our ongoing analysis (with crowd-sourced data) against different vehicle makes and models has revealed that ∼ 50% of the examined vehicles in the Asian region are vulnerable to RollBack, whereas the impact tends to be smaller in other regions, such as Europe and North America.

GPU cryptojacking is an attack that hijacks GPU resources of victims for cryptocurrency mining. Such attack is becoming an emerging threat to both local hosts and cloud platforms. These attacks result in huge economic losses for the victims due to significant power consumption by cryptomining applications. Unfortunately, there are no adequate solutions to detect such attacks. In this paper, we propose MagTracer, a novel GPU cryptojacking detection system that leverages magnetic leakage signals emanating from GPUs. We make a key observation that GPUs emanate a distinct magnetic signal while mining, which can be attributed to the core feature of all cryptomining algorithms (as they are compute-intensive as well as memory-bounded). We design and implement a proof-of-concept detection system to demonstrate MagTracer's feasibility. We evaluate MagTracer on 14 heterogeneous GPU models and achieve a high average true positive rate of over 98% and a low false positive rate below 0.7% in all cases. Furthermore, our comprehensive evaluation confirms that MagTracer is scalable across different mining applications and robust against several targeted attacks.

We are witnessing a heightened surge in remote privacy attacks on laptop computers. These attacks often exploit malware to remotely gain access to webcams and microphones in order to spy on the victim users. While webcam attacks are somewhat defended with widely available commercial webcam privacy covers, unfortunately, there are no adequate solutions to thwart the attacks on mics despite recent industry efforts. As a first step towards defending against such attacks on laptop mics, we propose TickTock, a novel mic on/off status detection system. To achieve this, TickTock externally probes the electromagnetic (EM) emanations that stem from the connectors and cables of the laptop circuitry carrying mic clock signals. This is possible because the mic clock signals are only input during the mic recording state, causing resulting emanations. We design and implement a proof-of-concept system to demonstrate TickTock's feasibility. Furthermore, we comprehensively evaluate TickTock on a total of 30 popular laptops executing a variety of applications to successfully detect mic status in 27 laptops. Of these, TickTock consistently identifies mic recording with high true positive and negative rates.

Delivery drones may become potential targets for package theft. An adversary may launch a drone impersonation attack, where the adversary's drone purports to be a legitimate delivery drone. To protect against such attacks, authenticating drones is crucial. Existing authentication schemes based on digital certificates have been shown to be compromised by security breaches on certificate authorities. Thus, we propose SoundUAV as a second factor of authentication for drones that leverages uniqueness in acoustic emanations to fingerprint drones, even within the same make and model. This uniqueness is attributed to hardware defects in motors, making SoundUAV secure against impersonation and robust to large scale attacks. Further, SoundUAV requires no hardware modifications to drones as it utilizes the pervasive acoustic emanations. We perform preliminary evaluation on eleven drones and obtain a fingerprinting accuracy of 99.48%.