User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.

Wave impedance selection for passivity-based bilateral teleoperation

When a task must be executed in a remote or dangerous environment, teleoperation systems may be employed to extend the influence of the human operator. In the case of manipulation tasks, haptic feedback of the forces experienced by the remote (slave) system is often highly useful in improving an operator's ability to perform effectively. In many of these cases (especially teleoperation over the internet and ground-to-space teleoperation), substantial communication latency exists in the control loop and has the strong tendency to cause instability of the system. The first viable solution to this problem in the literature was based on a scattering/wave transformation from transmission line theory. This wave transformation requires the designer to select a wave impedance parameter appropriate to the teleoperation system. It is widely recognized that a small value of wave impedance is well suited to free motion and a large value is preferable for contact tasks. Beyond this basic observation, however, very little guidance exists in the literature regarding the selection of an appropriate value. Moreover, prior research on impedance selection generally fails to account for the fact that in any realistic contact task there will simultaneously exist contact considerations (perpendicular to the surface of contact) and quasi-free-motion considerations (parallel to the surface of contact). The primary contribution of the present work is to introduce an approximate linearized optimum for the choice of wave impedance and to apply this quasi-optimal choice to the Cartesian reality of such a contact task, in which it cannot be expected that a given joint will be either perfectly normal to or perfectly parallel to the motion constraint. The proposed scheme selects a wave impedance matrix that is appropriate to the conditions encountered by the manipulator. This choice may be implemented as a static wave impedance value or as a time-varying choice updated according to the instantaneous conditions encountered. A Lyapunov-like analysis is presented demonstrating that time variation in wave impedance will not violate the passivity of the system. Experimental trials, both in simulation and on a haptic feedback device, are presented validating the technique. Consideration is also given to the case of an uncertain environment, in which an a priori impedance choice may not be possible.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.

MANIPULATION OF IONS, ELECTRONS, AND PHOTONS IN 2D MATERIALS BY ION INTERCALATION

2D materials have attracted tremendous attention due to their unique physical and chemical properties since the discovery of graphene. Despite these intrinsic properties, various modification methods have been applied to 2D materials that yield even more exciting results. Among all modification methods, the intercalation of 2D materials provides the highest possible doping and/or phase change to the pristine 2D materials. This doping effect highly modifies 2D materials, with extraordinary electrical transport as well as optical, thermal, magnetic, and catalytic properties, which are advantageous for optoelectronics, superconductors, thermoelectronics, catalysis and energy storage applications. To study the property changes of 2D materials, we designed and built a planar nanobattery that allows electrochemical ion intercalation in 2D materials. More importantly, this planar nanobattery enables characterization of electrical, optical and structural properties of 2D materials in situ and real time upon ion intercalation. With this device, we successfully intercalated Li-ions into few layer graphene (FLG) and ultrathin graphite, heavily dopes the graphene to 0.6 x 10^15 /cm2, which simultaneously increased its conductivity and transmittance in the visible range. The intercalated LiC6 single crystallite achieved extraordinary optoelectronic properties, in which an eight-layered Li intercalated FLG achieved transmittance of 91.7% (at 550 nm) and sheet resistance of 3 ohm/sq. We extend the research to obtain scalable, printable graphene based transparent conductors with ion intercalation. Surfactant free, printed reduced graphene oxide transparent conductor thin film with Na-ion intercalation is obtained with transmittance of 79% and sheet resistance of 300 ohm/sq (at 550 nm). The figure of merit is calculated as the best pure rGO based transparent conductors. We further improved the tunability of the reduced graphene oxide film by using two layers of CNT films to sandwich it. The tunable range of rGO film is demonstrated from 0.9 um to 10 um in wavelength. Other ions such as K-ion is also studied of its intercalation chemistry and optical properties in graphitic materials. We also used the in situ characterization tools to understand the fundamental properties and improve the performance of battery electrode materials. We investigated the Na-ion interaction with rGO by in situ Transmission electron microscopy (TEM). For the first time, we observed reversible Na metal cluster (with diameter larger than 10 nm) deposition on rGO surface, which we evidenced with atom-resolved HRTEM image of Na metal and electron diffraction pattern. This discovery leads to a porous reduced graphene oxide sodium ion battery anode with record high reversible specific capacity around 450 mAh/g at 25mA/g, a high rate performance of 200 mAh/g at 250 mA/g, and stable cycling performance up to 750 cycles. In addition, direct observation of irreversible formation of Na2O on rGO unveils the origin of commonly observed low 1st Columbic Efficiency of rGO containing electrodes. Another example for in situ characterization for battery electrode is using the planar nanobattery for 2D MoS2 crystallite. Planar nanobattery allows the intrinsic electrical conductivity measurement with single crystalline 2D battery electrode upon ion intercalation and deintercalation process, which is lacking in conventional battery characterization techniques. We discovered that with a “rapid-charging” process at the first cycle, the lithiated MoS2 undergoes a drastic resistance decrease, which in a regular lithiation process, the resistance always increases after lithiation at its final stage. This discovery leads to a 2- fold increase in specific capacity with with rapid first lithiated MoS2 composite electrode material, compare with the regular first lithiated MoS2 composite electrode material, at current density of 250 mA/g.