Currently, many automated vehicle systems primarily perceive the environment from a single perspective and as a result are unable to leverage additional scene information from the viewpoint of other vehicles on the road using vehicleto-vehicle communication technologies. We study how increased data sharing can improve the perception capabilities of automated vehicles. Our methodology shares sensor measurements and objects detected by state-of-the-art deep learning networks between vehicles to increase the automated driving systems confidence of detecting objects using a 3D sensor fusion algorithm. This approach can benefit scenarios where an object may be occluded (fully or partially) or located too far away to classify accurately by a single vehicle alone.

Current post-mortem cyber-forensic techniques may cause significant disruption to the evidence gathering process by breaking active network connections and unmounting encrypted disks. Although newer live forensic analysis tools can preserve active state, they may taint evidence by leaving footprints in memory. To help address these concerns we present Forenscope, a framework that allows an investigator to examine the state of an active system without the effects of taint or forensic blurriness caused by analyzing a running system. We show how Forenscope can fit into accepted workflows to improve the evidence gathering process.

With the advent of high-performance COTS clusters, there is a need for a simple, scalable and fault-tolerant parallel programming and execution paradigm. In this paper, we show that the popular MapReduce programming model can be utilized to solve many interesting scientific simulation problems with much higher performance than regular cluster computers by leveraging GPGPU accelerators in cluster nodes. We use the Massive Unordered Distributed (MUD) formalism and establish a one-to-one correspondence between it and general Monte Carlo simulation methods. Our architecture, MITHRA, leverages NVIDIA CUDA technology along with Apache Hadoop to produce scalable performance gains using the MapReduce programming model. The evaluation of our proposed architecture using the Black Scholes option pricing model shows that a MITHRA cluster of 4 GPUs can outperform a regular cluster of 62 nodes, achieving a speedup of about 254 times in our testbed, while providing scalable near linear performance with additional nodes.

User studies usually involve much organization and manual labor. Even when performed correctly, a common problem of the studies is user bias. This occurs when participating users' knowledge of the study influences their actions. Another problem is the willingness of the users to participate at all. Finally, participants will always have privacy concerns. We have developed a framework to help with anonymized user studies, trying to solve the aforementioned problems. We have prepared a demo to show the effectiveness of our system.

BootJacker is a proof-of-concept attack tool which demonstrates that authentication mechanisms employed by an operating system can be bypassed by obtaining physical access and simply forcing a restart. The key insight that enables this attack is that the contents of memory on some machines are fully preserved across a warm boot. Upon a reboot, BootJacker uses this residual memory state to revive the original host operating system environment and run malicious payloads. Using BootJacker, an attacker can break into a locked user session and gain access to open encrypted disks, web browser sessions or other secure network connections. BootJacker's non-persistent design makes it possible for an attacker to leave no traces on the victim machine.

Rootkits are used by malicious attackers who desire to run software on a compromised machine without being detected. They have become stealthier over the years as a consequence of the ongoing struggle between attackers and system defenders. In order to explore the next step in rootkit evolution and to build strong defenses, we look at this issue from the point of view of an attacker. We construct Cloaker, a proof-of-concept rootkit for the ARM platform that is non- persistent and only relies on hardware state modifications for concealment and operation. A primary goal in the design of Cloaker is to not alter any part of the host operating system (OS) code or data, thereby achieving immunity to all existing rootkit detection techniques which perform integrity, behavior and signature checks of the host OS. Cloaker also demonstrates that a self-contained execution environment for malicious code can be provided without relying on the host OS for any services. Integrity checks of hardware state in each of the machine's devices are required in order to detect rootkits such as Cloaker. We present a framework for the Linux kernel that incorporates integrity checks of hardware state performed by device drivers in order to counter the threat posed by rootkits such as Cloaker.

The users of today's operating systems demand high reliability and security. However, faults introduced outside of the core operating system by buggy and malicious device drivers can significantly impact these dependability attributes. To help improve driver isolation, we propose an approach that utilizes the latest hardware virtualization support to efficiently sandbox each device driver in its own minimal virtual machine (VM) so that the kernel is protected from faults in these drivers. We present our implementation of a low-overhead virtual-machine based framework which allows reuse of existing drivers. We have constructed a prototype to demonstrate that it is feasible to utilize existing hardware virtualization techniques to allow device drivers in a VM to communicate with devices directly without frequent hardware traps into the virtual machine monitor (VMM). We have implemented a prototype parallel port driver which interacts through iKernel to communicate with a physical LED device.

Established as a research project at the University of Illinois in the spring of 2002, eFuzion has proven to be a valuable and effective pedagogical set of tools. It provides the capacity to both mentor and assess students individually, both during and outside of class. In the summer of 2002, a study we conducted revealed that eFuzion's in-class tools increased student's final grade by more than 6 points. In this paper we describe the evolution of our system and experiences leading up to our "Classroom of the Future" demo for the Grand Opening of the Thomas M. Siebel Center for Computer Science. We conclude with a discussion of our future plans for eFuzion.

The Gaia ubiquitous computing platform currently supports mobile devices through a thin client proxy architecture. Mobile devices run a lightweight proxy client written in J2ME to join an active space. While this approach allows a wide variety of devices to interact with active spaces, it lacks the ability to use device specific functionality. This problem is addressed by combining the J2ME client with a microserver, which is a bridge from the native language to J2ME. The microserver-proxy approach enables thin clients to fully access device-specific features while respecting security through a standard interface as Gaia services.