Network attackers have increasingly resorted to proxy chains, VPNs, and anonymity networks to conceal their activities. To tackle this issue, past research has explored the use of traffic correlation techniques to perform attack attribution, i.e., to identify an attacker’s true network location. However, current traffic correlation approaches rely on well-provisioned and centralized systems that ingest flows from multiple network probes to compute correlation scores. Unfortunately, this makes correlation efforts scale poorly for large high-speed networks. In this paper, we propose RevealNet, a decentralized framework for attack attribution that orchestrates a fleet of P4programmable switches to perform traffic correlation. We build on top of a set of correlation primitives inspired by prior work on computing and comparing flow sketches-compact summaries of flows’ characteristics-to enable efficient, distributed, in-network traffic correlation. Our evaluation suggests that RevealNet achieves comparable accuracy to centralized attack attribution systems while significantly reducing the computational complexity and bandwidth overheads imposed by correlation tasks.

Many augmented reality (AR) applications operate within near-field reaching distances, and require matching the depth of a virtual object with a real object. The accuracy of this matching was measured in three experiments, which examined the effect of focal distance, age, and brightness, within distances of 33.3 to 50 cm, using a custom-built AR haploscope. Experiment I examined the effect of focal demand, at the levels of collimated (infinite focal distance), consistent with other depth cues, and at the midpoint of reaching distance. Observers were too young to exhibit age-related reductions in accommodative ability. The depth matches of collimated targets were increasingly overestimated with increasing distance, consistent targets were slightly underestimated, and midpoint targets were accurately estimated. Experiment II replicated Experiment I, with older observers. Results were similar to Experiment I. Experiment III replicated Experiment I with dimmer targets, using young observers. Results were again consistent with Experiment I, except that both consistent and midpoint targets were accurately estimated. In all cases, collimated results were explained by a model, where the collimation biases the eyes' vergence angle outwards by a constant amount. Focal demand and brightness affect near-field AR depth matching, while age-related reductions in accommodative ability have no effect.

Learning how to analyze computational problems, to think critically, and to transfer algorithmic logic into language-specific code is central to computer programming. A critical step towards acquiring these skills is analyzing and debugging existing code usually starting with the famous "Hello World" program.

Many compelling augmented reality (AR) applications require users to correctly perceive the location of virtual objects, some with accuracies as tight as 1 mm. However, measuring the perceived depth of AR objects at these accuracies has not yet been demonstrated. In this paper, we address this challenge by employing two different depth judgment methods, perceptual matching and blind reaching, in a series of three experiments, where observers judged the depth of real and AR target objects presented at reaching distances. Our experiments found that observers can accurately match the distance of a real target, but when viewing an AR target through collimating optics, their matches systematically overestimate the distance by 0.5 to 4.0 cm. However, these results can be explained by a model where the collimation causes the eyes' vergence angle to rotate outward by a constant angular amount. These findings give error bounds for using collimating AR displays at reaching distances, and suggest that for these applications, AR displays need to provide an adjustable focus. Our experiments further found that observers initially reach ∼4 cm too short, but reaching accuracy improves with both consistent proprioception and corrective visual feedback, and eventually becomes nearly as accurate as matching.

A previous study indicated that peripheral visual information strongly affects the judgment of egocentric distances for users of immersive virtual environments. The experiment described in this document aimed to investigate if these effects could be explained in terms of changes in gait caused by visual information in the extreme periphery. Three conditions with varying degrees of peripheral occlusion were tested and participants' walking characteristics measured. The results indicate that the improvements in distance judgments, as peripheral information increases, can only partially be explained in terms of gait modification, but likely involve both changes in the characteristics of gait and other spatial or movement parameters.

In this abstract we describe an experiment that measured depth judgments in optical see-through augmented reality (AR) at near-field reaching distances of ~ 24 to ~ 56 cm. The 2×2 experiment crossed two depth judgment tasks, perceptual matching and blind reaching, with two different environments, a real-world environment and an augmented reality environment. We designed a task that used a direct reaching gesture at constant percentages of each participant's maximum reach; our task was inspired by previous work by Tresilian and Mon-Williams [6] that found very accurate blind reaching results in a real-world environment.

A frequently observed problem in virtual environments is the underestimation of egocentric depth. This problem has been described numerous times and with widely varying degrees of severity. Though there has been considerable progress made in modifying observer behavior to compensate for these misperceptions, the question of why these errors exist is still an open issue. The study detailed in this document presents the preliminary findings of a large, between-subjects experiment (N=98) that attempts to identify and quantify the source of a pattern of adaptation and improved accuracy in the absence of explicit feedback found in Jones et al. [1].

In this poster abstract we describe an experiment that measured depth judgments in optical see-through augmented reality at near-field distances of 34 to 50 centimeters. The experiment compared two depth judgment tasks: perceptual matching, a closed-loop task, and blind reaching, a visually open-loop task. The experiment tested each of these tasks in both a real-world environment and an augmented reality environment, and used a between-subjects design that included 40 participants. The experiment found that matching judgments were very accurate in the real world, with errors on the order of millimeters and very little variance. In contrast, matching judgments in augmented reality showed a linear trend of increasing overestimation with increasing distance, with a mean overestimation of ~1 cm. With reaching judgments participants underestimated ~4.5 cm in both augmented reality and the real world. We also discovered and solved a calibration problem that arises at near-field distances.

A large number of previous studies have shown that egocentric depth perception tends to be underestimated in virtual reality (VR) - objects appear smaller and farther away than they should. Various theories as to why this might occur have been investigated, but to date the cause is not fully understood. A much smaller number of studies have investigated how depth perception operates in augmented reality (AR), and some of these studies have also indicated a similar underestimation effect. In this paper we report an experiment that further investigates these effects. The experiment compared VR and AR conditions to two real-world control conditions, and studied the effect of motion parallax across all conditions. Our combined VR and AR head-mounted display (HMD) allowed us to develop very careful calibration procedures based on real-world calibration widgets, which cannot be replicated with VR-only HMDs. To our knowledge, this is the first study to directly compare VR and AR conditions as part of the same experiment.