Systemization of Pluggable Transports for Censorship Resistance

An increasing number of countries implement Internet censorship at different scales and for a variety of reasons. In particular, the link between the censored client and entry point to the uncensored network is a frequent target of censorship due to the ease with which a nation-state censor can control it. A number of censorship resistance systems have been developed thus far to help circumvent blocking on this link, which we refer to as link circumvention systems (LCs). The variety and profusion of attack vectors available to a censor has led to an arms race, leading to a dramatic speed of evolution of LCs. Despite their inherent complexity and the breadth of work in this area, there is no systematic way to evaluate link circumvention systems and compare them against each other. In this paper, we (i) sketch an attack model to comprehensively explore a censor's capabilities, (ii) present an abstract model of a LC, a system that helps a censored client communicate with a server over the Internet while resisting censorship, (iii) describe an evaluation stack that underscores a layered approach to evaluate LCs, and (iv) systemize and evaluate existing censorship resistance systems that provide link circumvention. We highlight open challenges in the evaluation and development of LCs and discuss possible mitigations.Comment: Content from this paper was published in Proceedings on Privacy Enhancing Technologies (PoPETS), Volume 2016, Issue 4 (July 2016) as "SoK: Making Sense of Censorship Resistance Systems" by Sheharbano Khattak, Tariq Elahi, Laurent Simon, Colleen M. Swanson, Steven J. Murdoch and Ian Goldberg (DOI 10.1515/popets-2016-0028

Modeling Probability of Path Loss for DSDV, OLSR and DYMO above 802.11 and 802.11p

This paper presents path loss model along with framework for probability distribution function for VANETs. Furthermore, we simulate three routing protocols Destination Sequenced Distance Vector (DSDV), Optimized Link State Routing (OLSR) and Dynamic MANET On-demand (DYMO) in NS-2 to evaluate and compare their performance using two Mac-layer Protocols 802.11 and 802.11p. A novel approach of this work is modifications in existing parameters to achieve high efficiency. After extensive simulations, we observe that DSDV out performs with 802.11p while DYMO gives best performance with 802.11.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

Heterogeneous Ensemble Learning for Enhanced Crash Forecasts -- A Frequentest and Machine Learning based Stacking Framework

A variety of statistical and machine learning methods are used to model crash frequency on specific roadways with machine learning methods generally having a higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM), including stacking, have emerged as more accurate and robust intelligent techniques and are often used to solve pattern recognition problems by providing more reliable and accurate predictions. In this study, we apply one of the key HEM methods, Stacking, to model crash frequency on five lane undivided segments (5T) of urban and suburban arterials. The prediction performance of Stacking is compared with parametric statistical models (Poisson and negative binomial) and three state of the art machine learning techniques (Decision tree, random forest, and gradient boosting), each of which is termed as the base learner. By employing an optimal weight scheme to combine individual base learners through stacking, the problem of biased predictions in individual base-learners due to differences in specifications and prediction accuracies is avoided. Data including crash, traffic, and roadway inventory were collected and integrated from 2013 to 2017. The data are split into training, validation, and testing datasets. Estimation results of statistical models reveal that besides other factors, crashes increase with density (number per mile) of different types of driveways. Comparison of out-of-sample predictions of various models confirms the superiority of Stacking over the alternative methods considered. From a practical standpoint, stacking can enhance prediction accuracy (compared to using only one base learner with a particular specification). When applied systemically, stacking can help identify more appropriate countermeasures.Comment: This paper was presented at the 101st Transportation Research Board Annual Meeting (TRBAM) by National Academy of Sciences in January 2022 in Washington D.C. The paper is currently under review for potential publication in an Impact Factor Journa

Safety and Economic Assessment of Converting Two-Way Stop-Controlled Intersections to Roundabouts on High Speed Rural Highways

This research addressed two questions: “Are roundabouts on rural high-speed roadways safer than two-way stop controlled (TWSC) intersections?” and “What economic benefits can be expected from converting TWSC intersections to roundabouts in terms of safety improvement?” Crash and traffic data on four TWSC intersections that were converted to roundabouts in Kansas were analyzed using the empirical Bayes before-after evaluation method and crash costs were applied to evaluate economic benefits. Analysis showed that fatal, non-fatal, and property-damage-only crashes were reduced by 100%, 76.47%, and 35.49%, respectively. The annual monetary value from this reduction was between $1.0—$1.6 million in 2014 dollars