Biblio

In recent years, biometric techniques (e.g., fingerprint or iris) are increasingly integrated into mobile devices to offer security advantages over traditional practices (e.g., passwords and PINs) due to their ease of use in user authentication. However, existing biometric systems are with controversy: once divulged, they are compromised forever - no one can grow a new fingerprint or iris. This work explores a truly cancelable brain-based biometric system for mobile platforms (e.g., smart headwear). Specifically, we present a new psychophysiological protocol via non-volitional brain response for trustworthy mobile authentication, with an application example of smart headwear. Particularly, we address the following research challenges in mobile biometrics with a theoretical and empirical combined manner: (1) how to generate reliable brain responses with sophisticated visual stimuli; (2) how to acquire the distinct brain response and analyze unique features in the mobile platform; (3) how to reset and change brain biometrics when the current biometric credential is divulged. To evaluate the proposed solution, we conducted a pilot study and achieved an f -score accuracy of 95.46% and equal error rate (EER) of 2.503%, thereby demonstrating the potential feasibility of neurofeedback based biometrics for smart headwear. Furthermore, we perform the cancelability study and the longitudinal study, respectively, to show the effectiveness and usability of our new proposed mobile biometric system. To the best of our knowledge, it is the first in-depth research study on truly cancelable brain biometrics for secure mobile authentication.

The increasing adoption of 3D printing in many safety and mission critical applications exposes 3D printers to a variety of cyber attacks that may result in catastrophic consequences if the printing process is compromised. For example, the mechanical properties (e.g., physical strength, thermal resistance, dimensional stability) of 3D printed objects could be significantly affected and degraded if a simple printing setting is maliciously changed. To address this challenge, this study proposes a model-free real-time online process monitoring approach that is capable of detecting and defending against the cyber-physical attacks on the firmwares of 3D printers. Specifically, we explore the potential attacks and consequences of four key printing attributes (including infill path, printing speed, layer thickness, and fan speed) and then formulate the attack models. Based on the intrinsic relation between the printing attributes and the physical observations, our defense model is established by systematically analyzing the multi-faceted, real-time measurement collected from the accelerometer, magnetometer and camera. The Kalman filter and Canny filter are used to map and estimate three aforementioned critical toolpath information that might affect the printing quality. Mel-frequency Cepstrum Coefficients are used to extract features for fan speed estimation. Experimental results show that, for a complex 3D printed design, our method can achieve 4% Hausdorff distance compared with the model dimension for infill path estimate, 6.07% Mean Absolute Percentage Error (MAPE) for speed estimate, 9.57% MAPE for layer thickness estimate, and 96.8% accuracy for fan speed identification. Our study demonstrates that, this new approach can effectively defend against the cyber-physical attacks on 3D printers and 3D printing process.

Additive manufacturing, also known as 3D printing, has been increasingly applied to fabricate highly intellectual property (IP) sensitive products. However, the related IP protection issues in 3D printers are still largely underexplored. On the other hand, smartphones are equipped with rich onboard sensors and have been applied to pervasive mobile surveillance in many applications. These facts raise one critical question: is it possible that smartphones access the side-channel signals of 3D printer and then hack the IP information? To answer this, we perform an end-to-end study on exploring smartphone-based side-channel attacks against 3D printers. Specifically, we formulate the problem of the IP side-channel attack in 3D printing. Then, we investigate the possible acoustic and magnetic side-channel attacks using the smartphone built-in sensors. Moreover, we explore a magnetic-enhanced side-channel attack model to accurately deduce the vital directional operations of 3D printer. Experimental results show that by exploiting the side-channel signals collected by smartphones, we can successfully reconstruct the physical prints and their G-code with Mean Tendency Error of 5.87% on regular designs and 9.67% on complex designs, respectively. Our study demonstrates this new and practical smartphone-based side channel attack on compromising IP information during 3D printing.

In recent years, the analog-to-information converter (AIC), based on compressed sensing (CS) paradigm, is a promising solution to overcome the performance and energy-efficiency limitations of traditional analog-to-digital converters (ADC). Especially, AIC can enable sub-Nyquist signal sampling proportional to the intrinsic information in biomedical applications. However, the legacy AIC structure is tailored toward specific applications, which lacks of flexibility and prevents its universality. In this paper, we introduce a novel programmable AIC architecture, Pro-AIC, to enable effective configurability and reduce its energy overhead by integrating efficient multiplexing hardware design. To improve the quality and time-efficiency of Pro-AIC configuration, we also develop a rapid configuration algorithm, called RapSpiral, to quickly find the near-optimal parameter configuration in Pro-AIC architecture. Specifically, we present a design metric, trade-off penalty, to quantitatively evaluate the performance-energy trade-off. The RapSpiral controls a penalty-driven shrinking triangle to progressively approximate to the optimal trade-off. Our proposed RapSpiral is with log(n) complexity yet high accuracy, without pretraining and complex parameter tuning procedure. RapSpiral is also probable to avoid the local minimum pitfalls. Experimental results indicate that our RapSpiral algorithm can achieve more than 30x speedup compared with the brute force algorithm, with only about 3% trade-off compromise to the optimum in Pro-AIC. Furthermore, the scalability is also verified on larger size benchmarks.