Data-Efficient Quickest Outlying Sequence Detection in Sensor Networks

A sensor network is considered where at each sensor a sequence of random variables is observed. At each time step, a processed version of the observations is transmitted from the sensors to a common node called the fusion center. At some unknown point in time the distribution of observations at an unknown subset of the sensor nodes changes. The objective is to detect the outlying sequences as quickly as possible, subject to constraints on the false alarm rate, the cost of observations taken at each sensor, and the cost of communication between the sensors and the fusion center. Minimax formulations are proposed for the above problem and algorithms are proposed that are shown to be asymptotically optimal for the proposed formulations, as the false alarm rate goes to zero. It is also shown, via numerical studies, that the proposed algorithms perform significantly better than those based on fractional sampling, in which the classical algorithms from the literature are used and the constraint on the cost of observations is met by using the outcome of a sequence of biased coin tosses, independent of the observation process.Comment: Submitted to IEEE Transactions on Signal Processing, Nov 2014. arXiv admin note: text overlap with arXiv:1408.474

FilteredWeb: A Framework for the Automated Search-Based Discovery of Blocked URLs

Various methods have been proposed for creating and maintaining lists of potentially filtered URLs to allow for measurement of ongoing internet censorship around the world. Whilst testing a known resource for evidence of filtering can be relatively simple, given appropriate vantage points, discovering previously unknown filtered web resources remains an open challenge. We present a new framework for automating the process of discovering filtered resources through the use of adaptive queries to well-known search engines. Our system applies information retrieval algorithms to isolate characteristic linguistic patterns in known filtered web pages; these are then used as the basis for web search queries. The results of these queries are then checked for evidence of filtering, and newly discovered filtered resources are fed back into the system to detect further filtered content. Our implementation of this framework, applied to China as a case study, shows that this approach is demonstrably effective at detecting significant numbers of previously unknown filtered web pages, making a significant contribution to the ongoing detection of internet filtering as it develops. Our tool is currently deployed and has been used to discover 1355 domains that are poisoned within China as of Feb 2017 - 30 times more than are contained in the most widely-used public filter list. Of these, 759 are outside of the Alexa Top 1000 domains list, demonstrating the capability of this framework to find more obscure filtered content. Further, our initial analysis of filtered URLs, and the search terms that were used to discover them, gives further insight into the nature of the content currently being blocked in China.Comment: To appear in "Network Traffic Measurement and Analysis Conference 2017" (TMA2017

Distributed Detection of Cycles

Distributed property testing in networks has been introduced by Brakerski and Patt-Shamir (2011), with the objective of detecting the presence of large dense sub-networks in a distributed manner. Recently, Censor-Hillel et al. (2016) have shown how to detect 3-cycles in a constant number of rounds by a distributed algorithm. In a follow up work, Fraigniaud et al. (2016) have shown how to detect 4-cycles in a constant number of rounds as well. However, the techniques in these latter works were shown not to generalize to larger cycles $C_k$ with $k\geq 5$. In this paper, we completely settle the problem of cycle detection, by establishing the following result. For every $k\geq 3$, there exists a distributed property testing algorithm for $C_k$-freeness, performing in a constant number of rounds. All these results hold in the classical CONGEST model for distributed network computing. Our algorithm is 1-sided error. Its round-complexity is $O(1/\epsilon)$ where $\epsilon\in(0,1)$ is the property testing parameter measuring the gap between legal and illegal instances

A Churn for the Better: Localizing Censorship using Network-level Path Churn and Network Tomography

Recent years have seen the Internet become a key vehicle for citizens around the globe to express political opinions and organize protests. This fact has not gone unnoticed, with countries around the world repurposing network management tools (e.g., URL filtering products) and protocols (e.g., BGP, DNS) for censorship. However, repurposing these products can have unintended international impact, which we refer to as "censorship leakage". While there have been anecdotal reports of censorship leakage, there has yet to be a systematic study of censorship leakage at a global scale. In this paper, we combine a global censorship measurement platform (ICLab) with a general-purpose technique -- boolean network tomography -- to identify which AS on a network path is performing censorship. At a high-level, our approach exploits BGP churn to narrow down the set of potential censoring ASes by over 95%. We exactly identify 65 censoring ASes and find that the anomalies introduced by 24 of the 65 censoring ASes have an impact on users located in regions outside the jurisdiction of the censoring AS, resulting in the leaking of regional censorship policies

On Modeling the Costs of Censorship

We argue that the evaluation of censorship evasion tools should depend upon economic models of censorship. We illustrate our position with a simple model of the costs of censorship. We show how this model makes suggestions for how to evade censorship. In particular, from it, we develop evaluation criteria. We examine how our criteria compare to the traditional methods of evaluation employed in prior works