Origin of Critical Behavior in Ethernet Traffic

We perform a simplified Ethernet traffic simulation in order to clarify the physical mechanism of the phase transition behavior which has been experimentally observed in the flow density fluctuation of Internet traffic. In one phase traffics from nodes connected with an Ethernet cable are mixed, and in the other phase, the nodes alternately send bursts of packets. The competition of sending packets among nodes and the binary exponential back-off algorithm are revealed to play important roles in producing $1/f$ fluctuations at the critical point.Comment: 14 pages, 9 figures. To appear physica

Towards Modeling of Traffic Demand of Node in Large Scale Network

Abstract—Understanding actual network and traffic proper-ties of the Internet is essential to determine network parame-ters in large-scale network simulations. However, there is little knowledge about the distribution of macroscopic traffic demand for each node, though the topological properties of the network have been focused on. This paper investigates the distribution of traffic volume to and from a node at an organization level. As traffic volume data, we used byte counter data of all interfaces in all backbone routers in a nation-wide research and education (R&E) network in Japan. First, we show that traffic volumes to and from a node in the network are characterized by a lognormal distribution, which has a slower decay than a normal distribution, but a faster decay than a power-law distribution. Thus, an assumption in which the traffic demand is uniformly random or Gaussian distributed is not appropriated to model the traffic demand in large-scale network simulation. This finding implies that one has more possibility to observe an increase of delay or packet drop in simulation, comparing to the result that uses uniformly-random or Gaussian traffic demand, because of the locality of traffic. Moreover, we observed that in 87 % of nodes, a traffic volume from the backbone to the node is 1-10 times larger than that for the opposite direction. This is a similar usage pattern appeared in residential light-user broadband traffic. Finally, we introduce a simple model to explain the distribution of traffic demand, based on a multiplicative growth of traffic volume. We confirm that the multiplicative model can reproduce a lognormal distribution of traffic volume by simple numerical simulation. I

Rubber Hand Illusion under Delayed Visual Feedback

BACKGROUND: Rubber hand illusion (RHI) is a subject's illusion of the self-ownership of a rubber hand that was touched synchronously with their own hand. Although previous studies have confirmed that this illusion disappears when the rubber hand was touched asynchronously with the subject's hand, the minimum temporal discrepancy of these two events for attenuation of RHI has not been examined. METHODOLOGY/PRINCIPAL FINDINGS: In this study, various temporal discrepancies between visual and tactile stimulations were introduced by using a visual feedback delay experimental setup, and RHI effects in each temporal discrepancy condition were systematically tested. The results showed that subjects felt significantly greater RHI effects with temporal discrepancies of less than 300 ms compared with longer temporal discrepancies. The RHI effects on reaching performance (proprioceptive drift) showed similar conditional differences. CONCLUSIONS/SIGNIFICANCE: Our results first demonstrated that a temporal discrepancy of less than 300 ms between visual stimulation of the rubber hand and tactile stimulation to the subject's own hand is preferable to induce strong sensation of RHI. We suggest that the time window of less than 300 ms is critical for multi-sensory integration processes constituting the self-body image

ASTrack: Automatic detection and removal of web tracking code with minimal functionality loss

Recent advances in web technologies make it more difficult than ever to detect and block web tracking systems. In this work, we propose ASTrack, a novel approach to web tracking detection and removal. ASTrack uses an abstraction of the code structure based on Abstract Syntax Trees to selectively identify web tracking functionality shared across multiple web services. This new methodology allows us to: (i) effectively detect web tracking code even when using evasion techniques (e.g., obfuscation, minification, or webpackaging); and (ii) safely remove those portions of code related to tracking purposes without affecting the legitimate functionality of the website. Our evaluation with the top 10k most popular Internet domains shows that ASTrack can detect web tracking with high precision (98%), while discovering about 50k tracking code pieces and more than 3,400 new tracking URLs not previously recognized by most popular privacy-preserving tools (e.g., uBlock Origin). Moreover, ASTrack achieved a 36% reduction in functionality loss in comparison with the filter lists, one of the safest options available. Using a novel methodology that combines computer vision and manual inspection, we estimate that full functionality is preserved in more than 97% of the websites.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/ AEI/10.13039/501100011033. This work is also par-tially supported by the NII internship program.Peer ReviewedPostprint (author's final draft