Alex X. Liu - Selected Publications#

1. Wei Wang, Alex X. Liu, Muhammad Shahzad, Kang Ling, and Sanglu Lu. "Device-free Human Activity Recognition Using Commercial WiFi Devices". IEEE Journal on Selected Areas in Communications (JSAC), Vol. 35, No. 5, pages 1118-1131, January 2017. The conference version titled "Understanding and Modeling of WiFi Signal Based Human Activity Recognition" was published in the Proceedings of the 21th Annual International Conference on Mobile Computing and Networking (MOBICOM), pages 65-76, Paris, France, September 2015, acceptance rate: 38/207 = 18.4%.

The number of citations=701(conference version)+231(journal version)=934.

Significance: This work proposes the first model that can quantitatively correlate WiFi signal dynamics and human activities. Based on this model, this work proposes a WiFi signal based human activity recognition and monitoring scheme using Commercial Off-The-Shelf (COTS) devices, and achieves a recognition accuracy of 96% and the scheme is robust to environmental changes. Human activity recognition is the core technology that enables a wide variety of applications such as health care, smart homes, fitness tracking, and building surveillance. The key limitation of these WiFi based human activity recognition systems is the lack of a model that can quantitatively correlate WiFi signal dynamics and human activities. As such, these systems mostly rely on the statistical characteristics of WiFi signals, such as Doppler movement directions and distributions of signal strength, to distinguish different human activities. The lack of such a model limits the further development of WiFi based human activity recognition technologies as it is difficult to understand the correlation between WiFi signal dynamics and human activities. Furthermore, without such a model, it is difficult to optimize the performance of such systems due to the lack of adjustable parameters.

2. Kamran Ali, Alex X. Liu, Wei Wang, Muhammad Shahzad. "Recognizing Keystrokes Using WiFi Devices". IEEE Journal on Selected Areas in Communications (JSAC), Vol. 35, No. 5, pages 1175-1190, January 2017. The conference version titled "Keystroke Recognition Using WiFi Signals" was published in the Proceedings of the 21th ACM Annual International Conference on Mobile Computing and Networking (MOBICOM), Paris, France, September 2015, acceptance rate: 38/207 = 18.4%.

Number of citations=461(conference version)+77(journal version)=538.

Significance: Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. This work is the first that shows WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values. This work proposes the first WiFi signal-based keystroke recognition scheme called WiKey. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys.

3. Muhammad Shahzad, Alex X. Liu, and Arjmand Samuel. "Behavior Based Human Authentication on Touch Screen Devices Using Gestures and Signatures". IEEE Transactions on Mobile Computing (TMC), Vol 16, No. 10, Pages 2726-2741, October, 2017. The conference version titled "Secure Unlocking of Mobile Touch Screen Devices by Simple Gestures -- You can see it but you cannot do it" was published in the Proceedings of the 19th ACM Annual International Conference on Mobile Computing and Networking (MOBICOM), pages 39-50, Miami, Florida, September 2013, acceptance rate: 28/208 = 13.4%.

The number of citations=260(conference version)+45(journal version)=305.

Significance: Passwords/PINs/patterns based authentication schemes for mobile devices with touch screens are inherently vulnerable to shoulder surfing attacks and smudge attacks. This work proposes the first scheme for touch screen devices that authenticates users based on their behavior of performing certain actions on the touch screens. An action is either a gesture, which is a brief interaction of a user’s fingers with the touch screen such as swipe rightwards, or a signature, which is the conventional unique handwritten depiction of one’s name. Unlike existing authentication schemes for touch screen devices, which use what user inputs as the authentication secret, this scheme authenticates users mainly based on how they input, using distinguishing features such as velocity, device acceleration, and stroke time. Even if attackers see what action a user performs, they cannot reproduce the behavior of the user doing those actions through shoulder surfing or smudge attacks. Experimental results show that, with only 25 training samples, for gestures, this scheme achieves an average equal error rate of 0.5 percent with three gestures and for signatures, it achieves an average equal error rate of 0.52 percent with a single signature.

4. Longfei Shangguan, Zheng Yang, Alex X. Liu, Zimu Zhou, and Yunhao Liu. "STPP: Spatial-Temporal Phase Profiling-Based Method for Relative RFID Tag Localization". IEEE/ACM Transactions on Networking (ToN), Vol. 25, No.1, pages 596-609, February 2017. The conference version titled "Relative Localization of RFID Tags using Spatial-Temporal Phase Profiling" was published in the Proceedings of the 12nd USENIX Symposium on Networked Systems Design and Implementation (NSDI), Oakland, California, May, 2015, acceptance rate: 42/213 =19.7%.

The number of citations=106(conference version)+129(journal version)=235.

Significance: Although many schemes for object localization using radio frequency identification (RFID) tags have been proposed, they mostly focus on absolute object localization and are not suitable for relative object localization because of large error margins and the special hardware that they require. This work proposes the first scheme for RFID-based relative object localization. The basic idea of STPP is that by moving a reader over a set of tags during which the reader continuously interrogating the tags, for each tag, the reader obtains a sequence of RF phase values, which is called a phase profile, from the tag’s responses over time. By analyzing the spatial-temporal dynamics in the phase profiles, this scheme can calculate the spatial ordering among the tags. In comparison with prior absolute object localization schemes, this scheme requires neither dedicated infrastructure nor special hardware. This scheme achieves about 84% ordering accuracy for misplaced books and 95% ordering accuracy for baggage handling.

5. Wei Wang, Alex X. Liu, and Muhammad Shahzad. "Gait Recognition Using WiFi Signals". In Proceedings of the 18th ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), HeidelBerg, Germany, September 2016, acceptance rate: 115/481 = 23.9%.

The number of citations=355.

Significance: This work proposes the first scheme that uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans. The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the CSI on the WiFi receiver. To profile human movement using CSI, this work uses signal processing techniques to generate spectrograms from CSI measurements so that the resulting spectrograms are similar to those generated by specifically designed Doppler radars. To extract features from spectrograms that best characterize the walking pattern, this work performs autocorrelation on the torso reflection to remove imperfection in spectrograms. This scheme achieves top-1, top-2, and top-3 recognition accuracies of 79.28%, 89.52%, and 93.05%, respectively.

6. Wei Wang, Alex X. Liu, and Ke Sun. "Device-Free Gesture Tracking Using Acoustic Signals". In Proceedings of the 22th ACM Annual International Conference on Mobile Computing and Networking (MOBICOM), New York City, New York, October 2016, acceptance rate: 32/226 = 14.2%.

The number of citations=257.

Significance: Device-free gesture tracking is an enabling HCI mechanism for small wearable devices because fingers are too big to control the GUI elements on such small screens. This work proposes the first device-free gesture tracking scheme using acoustic signals. It can be deployed on existing mobile devices as software, without any hardware modification as it uses speakers and microphones that already exist on most mobile devices to perform device-free tracking of a hand/finger. The key idea is to use acoustic phase to get fine-grained movement direction and movement distance measurements. For 1-D hand movement and 2-D drawing in the air, LLAP has a tracking accuracy of 3.5mm and 4.6 mm, respectively. This scheme can recognize the characters and short words drawn in the air with an accuracy of 92.3% and 91.2%, respectively.

7. Chad R. Meiners, Alex X. Liu, and Eric Torng. "Bit Weaving: A Non-prefix Approach to Compressing Packet Classifiers in TCAMs". IEEE/ACM Transactions on Networking (ToN), Vol. 20, No. 2, pages 488-500, April 2012. The conference version titled "Bit Weaving: A Non-prefix Approach to Compressing Packet Classifiers in TCAMs" was published in the Proceedings of the 17th IEEE International Conference on Network Protocols (ICNP), pages 93-102, Princeton, New Jersey, October 2009, acceptance rate: 36/197=18.3%.

The number of citations=211.

Significance: Packet classification is the core mechanism that enables many networking devices, such as routers and firewalls, to perform services, such as packet filtering. Ternary content addressable memories (TCAMs) have become the de facto standard in industry for fast packet classification. Unfortunately, TCAMs have limitations of small capacity, high power consumption, high heat generation, and high cost. One approach for coping with these limitations is to use compression schemes to reduce the number of TCAM rules required to represent a classifier. This work proposes bit weaving, the first non-prefix TCAM compression scheme.

8. Alex X. Liu, Fei Chen, JeeHyun Hwang, and Tao Xie. "Designing Fast and Scalable XACML Policy Evaluation Engines". IEEE Transactions on Computers (TC), Vol. 60, No. 12, pages 1802 - 1817, December 2011. The conference version titled "XEngine: A Fast and Scalable XACML Policy Evaluation Engine" was published in the Proceedings of the ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), Pages 265-276, June 2008, acceptance rate: 36/201 = 18%.

The number of citations=157(conference version)+87(journal version)=244.

Significance: This work proposes the first fast policy evaluation algorithm for the OASIS standard access control language XACML. This algorithm is 4-5 orders of magnitude faster than the state-of-the-art. The code is open sourced. XACML is the industry standard for specifying access control policies. It has been widely supported by all the main platform vendors.

9. Alex X. Liu, Chad R. Meiners, and Eric Torng. "TCAM Razor: A Systematic Approach Towards Minimizing Packet Classifiers in TCAMs". IEEE/ACM Transactions on Networking, Vol. 18, No. 2, pages 490-500, April 2010. The conference version titled "TCAM Razor: A Systematic Approach Towards Minimizing Packet Classifiers in TCAMs" was published in the Proceedings of the 15th IEEE International Conference on Network Protocols (ICNP), pages 226-275, Beijing, China, October 2007, acceptance rate: 32/220=14.5%.

The number of citations=316.

Significance: This work proposes the fundamental concept of semantics-based Ternary Content Addressable Memory (TCAM) optimization, which outperforms prior syntax-based TCAM optimization by orders of magnitude. This work changed the research direction of the community from syntax-based to semantics-based. The firewall optimization algorithms have been used by many researchers (such as Member of National Academy of Engineering, ACM Fellow, IEEE Fellow, Professor Jenifer Rexford at Princeton University) in their work as a basic building block of their solutions for optimizing Software Defined Networking (SDN) rules.

10. M. Zubair Shafiq, Lusheng Ji, Alex X. Liu, Jeffrey Pang, and Jia Wang. "Large Scale Measurement and Characterization of Cellular Machine-to-Machine Traffic". IEEE/ACM Transactions on Networking (ToN), Vol. 21, No. 6, pages 1960-1973, December 2013. The conference version titled "A First Look at Cellular Machine-to-Machine Traffic - Large Scale Measurement and Characterization" was published in the Proceedings of the ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), pages 65-76, London, United Kingdom, June 2012, acceptance rate: 31/203 =15.3%.

The number of citations=323(conference version)+160(journal version)=483.

Significance: Cellular network based Machine-to-Machine (M2M) communication is fast becoming a market-changing force for a wide spectrum of businesses and applications such as telematics, smart metering, point-of-sale terminals, and home security and automation systems. This work conducted the first measurement study of the characteristics of M2M traffic and compare it with traditional smartphone traffic.