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Managing and Monitoring Landscapes Protecting and improving land health requires comprehensive landscape management strategies. Land managers have embraced a landscape-scale philosophy and have developed new methods to inform decision making such as satellite imagery to assess current conditions and detect changes, and predictive models to forecast change. The Landscape Toolbox is a coordinated system of tools and methods for implementing land health monitoring and integrating monitoring data into management decision-making.The goal of the Landscape Toolbox is to provide the tools, resources, and training to land health monitoring methods and technologies for answering land management questions at different scales.Nelson Stauffer Uncategorized 0The core methods described in the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems are intended for multiple use. Each method collects data that can be used to calculate multiple indicators and those indicators have broad applicability. Two of the vegetative methods, canopy gap and vegetation height, have direct application…Continue readingNelson Stauffer Uncategorized 0Quality Assurance (QA) and Quality Control (QC) are both critical to data quality in ecological research and both are often misunderstood or underutilized. QA is a set of proactive processes and procedures which prevent errors from entering a data set, e.g., training, written data collection protocols, standardized data entry formats,…Continue readingNelson Stauffer Uncategorized 0In order to meet its monitoring and information needs, the Bureau of Land Management is making use of its Assessment, Inventory, and Monitoring strategy (AIM). While taking advantage of the tools and approaches available on the Landscape Toolbox, there are additional implementation requirements concerning the particulars of sample design, data…Continue readingNelson Stauffer Methods Guide, Monitoring Manual, Training 0We’ve added two new videos demonstrating and explaining the Core Methods of Plant species inventory and Vegetation height to our collection. These are two methods that previously didn’t have reference videos, although the rules and procedures for both can be found in volume I of the Monitoring Manual for Grassland, Shrubland,…Continue readingSarah McCord Methods Guide, Monitoring Manual, Training 0Question: Are succulents counted as a woody species when measuring vegetation heights? Answer: Yes. Succulent plant species are considered to be woody in contrast to herbaceous because their function is more similar to woody vegetation than herbaceous vegetation in many applications of these data. From a wildlife viewpoint: Some succulents are…Continue readingNelson Stauffer Blog, News, Presentations 0The 68th annual Society for Range Management meeting held in the first week of February 2015 in Sacramento, California was a success for the Bureau of Land Management’s Assessment, Inventory, and Monitoring (AIM) strategy. Staff from the BLM’s National Operations Center and the USDA-ARS Jornada hosted a day-long symposium to…Continue readingJason Karl Blog, Sample Design sample design, sampling 0What is an Inference Space? Inference space can be defined in many ways, but can be generally described as the limits to how broadly a particular results applies (Lorenzen and Anderson 1993, Wills et al. in prep.). Inference space is analogous to the sampling universe or the population. All these…Continue readingNelson Stauffer Blog, Monitoring Tools & Databases, News 0A new version of the Database for Inventory, Monitoring, and Assessment has just been released! This latest iteration—as always—aims to improve stability and reliability for field data collection on a tablet and data report generation in the office. For more information about DIMA and how it fits into project designs,…Continue readingJason Karl Blog, News 0In compiling information for the redesign of the Landscape Toolbox website and the second edition of the Monitoring Manual, I kept referring back to a small set of seminal references. These are my “Go-To” books and papers for designing and implementing assessment, inventory, and monitoring programs and for measuring vegetation…Continue readingJason Karl Blog, News 0We’re excited to show off the new redesign of the Landscape Toolbox. We’re in the middle of not only refreshing the website, but also completely overhauling the content and how it’s organized in the Toolbox. This version of the Toolbox is draft at this point and is evolving rapidly. Take…Continue reading


The study is a collaboration between researchers Rebekah Overdorf1, Marc Juarez2, Gunes Acar2, Rachel Greenstadt1, Claudia Diaz2
1 Drexel University {rebekah.overdorf,rachel.a.greenstadt}@drexel.edu
2 imec-COSIC KU Leuven {marc.juarez, gunes.acar, claudia.diaz}@esat.kuleuven.be
Reference: R. Overdorf, M. Juarez, G. Acar, R. Greenstadt, C. Diaz. How Unique is Your .onion? An Analysis of the Fingerprintability of Tor Onion Services . In Proceedings of ACM Conference on Computer and Communications Security (CCS'17). ACM, Nov. 2017. (Forthcoming)Website fingerprinting attacks aim to uncover which web pages a target user visits. They apply supervised machine learning classifiers to network traffic traces to identify patterns that are unique to a web page. These attacks circumvent the protection afforded by encryption and the metadata protection of anonymity systems such as Tor.Website fingerprinting can be deployed by adversaries with modest resources who have access to the communications between the user and their connection to the Internet, or on an anonymity system like Tor, the entry guard (see the figure below). There are many entities in a position to access this communication including wifi router owners, local network administrators or eavesdroppers, Internet Service Providers, and Autonomous Systems, among other network intermediaries.Prior studies typically report average performance results for a given website fingerprinting method or countermeasure. However, if you own a hidden service, you are more concerned with the security of your particular hidden service than how well an attack or defense works overall. If your site is naturally hidden against attacks, then you do not need to implement a defense. Conversely, your site may not be protected by a certain defense, despite the high overall protection of such defense.In this study, we try to answer the following two questions:Are some websites more fingerprintable than others?If so, what makes them more (or less) fingerprintable?Disparate impact of website fingerprintingWe have identified high variance in the results obtained by the website fingerprinting state-of-the-art attacks (i.e., k-NN, CUMUL and k-FP) across different onion websites: some sites (such as the ones in the table below) have higher identification rates than others and, thus, are more vulnerable to website fingerprinting.The table below shows the top five onion services ranked by number of misclassifications. We observe a partial overlap between the sites that are most misclassified across different classifiers. This indicates the errors of these classifiers are correlated to some extent. We looked into these classifications in more detail..onion URLTPFPFNF1k-NN4fouc...484660.05ykrxn...362670.04wiki5k...377670.04ezxjj...276680.03newsi...187690.01CUMULzehli...215680.054ewrw...229680.04harry...229680.04sqtlu...235680.04yiy4k...114690.02k-FPykrxn...462660.06ykrxn...342670.05wiki5...355670.05jq77m...254680.03newsi...263680.03
Analysis of classification errorsWe have analyzed the misclassifications of the three state-of-the-art classifiers. In the following Venn diagram, each circle represents the set of prediction errors for one of the classifiers. In the intersections of these circles are the instances that were incorrectly classified by the overlapping methods. 31% of the erred instances were misclassified by all three methods, suggesting strong correlation in the errors.We looked into the misclassifications that fall in the intersection among the three classifiers to understand what features make them be consistently misclassified.Misclassification graphConfusion graph for the CUMUL classifier drawn by Gephi software using the methodology explained in the paper. Nodes are colored based on the community they belong to, which is determined by the Louvain community detection algorithm. Node size is drawn proportional to the node degree, that is, bigger node means lower classification accuracy. We observe highly connected communities on the top left, and the right which suggests clusters of Hidden Services which are commonly confused as each other. Further, we notice several node pairs that are commonly classified as each other, forming ellipses.Network-level featuresIn the figure below we plot the instances that fall in the intersection of the misclassification areas of the attacks in the Venn diagram. In the x-axis we plot the normalized median incoming size of the true site and, in the y-axis, we show the same feature for the site that the instance was confused with.Total incoming packet size can be thought as the size of the site, as most traffic in a web page download is incoming.We see that the sizes of the true and the predicted sites in the misclassifications are strongly correlated, indicating that sites that were misclassified had similar sizes.At the same time, the high density of instances (see the histograms at the margins of the figure) shows that the vast majority of sites that were misclassified are small.Site-level featuresThe figure below shows the results of the site-level feature analysis using information gain as feature importance metric. We see that features associated with the size of the site give the highest information gain for determining fingerprintability when all the sites are considered. Among the smallest sites, which are generally less identifiable, we see that standard deviation features are also important, implying that sites that are more dynamic are harder to fingerprint.ConclusionsWe have studied what makes certain sites more or less vulnerable to the attack. We examine which types of features are common in sites vulnerable to website fingerprinting attacks. We also note that from the perspective of an onion service provider, overall accuracies do not matter, only whether a particular defense will protect their site and their users.Our results can guide the designers and operators of onion services as to how to make their own sites less easily fingerprintable and inform design decisions for countermeasures, in particular considering the results of our feature analyses and misclassifications. For example, we show that the larger sites are reliably more identifiable, while the hardest to identify tend to be small and dynamic.. This includes crawling infrastructure, modules for analysing browser profile data and crawl datasets.

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