This work exposes a largely unexplored vector of physical-layer attacks with demonstrated consequences in automobiles. By modifying the physical environment around analog sensors such as Antilock Braking Systems (ABS), we exploit weaknesses in wheel speed sensors so that a malicious attacker can inject arbitrary measurements to the ABS computer which in turn can cause lifethreatening situations. In this paper, we describe the development of a prototype ABS spoofer to enable such attacks and the potential consequences of remaining vulnerable to these attacks. The class of sensors sensitive to these attacks depends on the physics of the sensors themselves. ABS relies on magnetic-based wheel speed sensors which are exposed to an external attacker from underneath the body of a vehicle. By placing a thin electromagnetic actuator near the ABS wheel speed sensors, we demonstrate one way in which an attacker can inject magnetic fields to both cancel the true measured signal and inject a malicious signal, thus spoofing the measured wheel speeds. The mounted attack is of a noninvasive nature, requiring no tampering with ABS hardware and making it harder for failure and/or intrusion detection mechanisms to detect the existence of such an attack. This development explores two types of attacks: a disruptive, naive attack aimed to corrupt the measured wheel speed by overwhelming the original signal and a more advanced spoofing attack, designed to inject a counter-signal such that the braking system mistakenly reports a specific velocity. We evaluate the proposed ABS spoofer module using industrial ABS sensors and wheel speed decoders, concluding by outlining the implementation and lifetime considerations of an ABS spoofer with real hardware.

Providing an accurate, low cost estimate of sub-room indoor positioning remains an active area of research with applications including reactive indoor spaces, dynamic temperature control, wireless health, and augmented reality, to name a few. Recently proposed indoor localization solutions have required anywhere from zero additional infrastructure to customized RF hardware and have provided room-level to centimeter-level accuracy, typically in that respective order. One emerging technology that is proving a pragmatic solution for scalable, accurate localization is that of Bluetooth Low Energy beaconing, spearheaded by Apple's recently introduced iBeacon protocol. In this demo, we present a suite of localization tools developed around the iBeacon protocol, providing an in-depth look at Bluetooth Low Energy's viability as an indoor positioning technology. Our system shows an average position estimation error of 0.53 meters.

Information about quantitative characteristics in local businesses and services, such as the number of people waiting in line in a cafe and the number of available fitness machines in a gym, is important for informed decision, crowd management and event detection. In this paper, we investigate the potential of leveraging crowds as sensors to report such quantitative characteristics and investigate how to recover the true quantity values from noisy crowdsourced information. Through experiments, we find that crowd sensors have both bias and variance in quantity sensing, and task difficulties impact the sensing accuracy. Based on these findings, we propose an unsupervised probabilistic model to jointly assess task difficulties, ability of crowd sensors and true quantity values. Our model differs from existing categorical truth finding models as ours is specifically designed to tackle quantitative truth. In addition to devising an efficient model inference algorithm in a batch mode, we also design an even faster online version for handling streaming data. Experimental results in various scenarios demonstrate the effectiveness of our model.

is demo abstract presents PrOLoc, a localization system that combines partially homomorphic encryption with a new way of structuring the localization problem to enable e cient and accurate computation of a target's location while preserving the privacy of the observers.