Kamran Ali

Keystroke Recognition Using WiFi Signals

Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that 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, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal based keystroke recognition system called WiKey. WiKey consists of two Commercial Off-The-Shelf (COTS) WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves more than 97.5\% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%.

Recognizing Keystrokes Using WiFi Devices

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. In this paper, we show for the first time that 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, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy.

Joint event detection & identification: A clustering based approach for Wireless Sensor Networks

Distributed clustering based techniques have been increasingly employed for outlier detection in Wireless Sensor Networks (WSNs). But despite its numerous advantages such as online and efficient computations and incorporation of spatiotemporal & attribute correlations, clustering has not been studied for event detection & identification, which is essential for smooth and reliable operations of large scale WSNs. This paper introduces the significance of clustering based event detection & identification to the research community. Further, it presents an online technique for joint event detection and identification that achieves a very high performance for synthetic and real data sets with a significant reduction in computational complexity as compared to the state-of-the-art techniques. A remarkable advantage of the proposed technique is that it can identify the key attributes in the ascending order of their contribution towards an event without incurring any additional complexity.

Composite event detection and identification for WSNs using General Hebbian Algorithm

In this paper, we propose an on-line technique for in-network, distributed and composite event detection and identification for streaming sensor data in resource constrained Wireless Sensor Networks (WSNs). We use General Hebbian Algorithm (GHA) to find out principal components of a multi-attribute input data which has a linear complexity as opposed to quadratic complexity with eigen value decomposition (EVD). This allows for on-line computation of percentage contributions of individual attributes towards detected event. Comparison with other event detection techniques shows that our scheme incurs low communication overhead as compared to some state-of-the-art schemes. Moreover, our hyper-ellipsoidal clustering based event detection algorithm is shown to achieve high detection rates (DRs) of over 98.88% and very low false positive rates (FPRs) of below 0.01%. Our simulation results and the hardware implementation also show that the accuracy of proposed identification scheme is in strong agreement with EVD based techniques, proving it to be a successful event identification method for WSNs.

Distributed Event Identification for WSNs in Non-Stationary Environments

This paper proposes a novel scheme to estimate the percentage contribution of different attributes in a detected event (a process termed as event identification) for streaming multi-attribute data in WSNs. The proposed event detection and identification algorithm takes into account correlation among sensed attributes as well as the spatio-temporal correlations with similar attributes measured by neighboring nodes. Moreover we update our statistical parameters in an iterative manner such that the dynamics of non- stationary environments are taken into account. We test our leave one out (LOO) event identification approach with simulations on both synthetic and real data sets and an implementation on off-the- shelf WizziMotes. The experimental results show that our detection scheme outperforms state of the art schemes by showing detection rates (DRs) of more than 98\% and false positive rates (FPRs) of less that 2\%. Moreover, our event identification approach effectively determines the contribution of both correlated and uncorrelated attributes in an event of interest. The identification has also been shown to be in strong agreement with previous computationally complex benchmark PCA based event identification approaches.

EveTrack: An event localization and tracking scheme for WSNs in dynamic environments

This paper introduces EveTrack, an online and distributed method for localization and tracking of global and composite events. Based on hyper-ellipsoid clustering model, we compute the percentage contributions of the individual attributes in multi-attribute and correlated events. In addition, EveTrack utilizes spatio-temporal correlations between multiple events during its event identification phase. Finally, EveTrack estimates the event location using an iterative Linear Least Square (LLS) approach based on the event intensities estimated at different nodes. The results of our localization algorithm show 4-10 fold improvement in localization accuracy with significantly less computational complexity when compared to previously proposed event localization algorithms.

Boosting powerline communications for ubiquitous connectivity in enterprises

Powerline communication (PLC) provides inexpensive, secure and high speed network connectivity, by leveraging the existing power distribution networks inside the buildings. While PLC technology has the potential to improve connectivity and is considered a key enabler for sensing, control, and automation applications in enterprises, it has been mainly deployed for improving connectivity in homes. Deploying PLCs in enterprises is more challenging since the power distribution network is more complex as compared to homes. Moreover, existing PLC technologies such as HomePlug AV have not been designed for and evaluated in enterprise deployments. To this end, we give guidlines for designing PLC networks for providing ubiquitous connectivity in enterprises, based on measurement study of PLC performance in enterprise settings using commodity HomePlug AV PLC devices. Based on our findings, we propose that careful planning of PLC network topology, routing and spectrum sharing can significantly boost performance of enterprise PLC networks.