k-fingerprinting: a Robust Scalable Website Fingerprinting Technique

Website fingerprinting enables an attacker to infer which web page a client is browsing through encrypted or anonymized network connections. We present a new website fingerprinting technique based on random decision forests and evaluate performance over standard web pages as well as Tor hidden services, on a larger scale than previous works. Our technique, k-fingerprinting, performs better than current state-of-the-art attacks even against website fingerprinting defenses, and we show that it is possible to launch a website fingerprinting attack in the face of a large amount of noisy data. We can correctly determine which of 30 monitored hidden services a client is visiting with 85% true positive rate (TPR), a false positive rate (FPR) as low as 0.02%, from a world size of 100,000 unmonitored web pages. We further show that error rates vary widely between web resources, and thus some patterns of use will be predictably more vulnerable to attack than others.Comment: 17 page

Panako: a scalable acoustic fingerprinting system handling time-scale and pitch modification

In this paper a scalable granular acoustic fingerprinting system robust against time and pitch scale modification is presented. The aim of acoustic fingerprinting is to identify identical, or recognize similar, audio fragments in a large set using condensed representations of audio signals, i.e. fingerprints. A robust fingerprinting system generates similar fingerprints for perceptually similar audio signals. The new system, presented here, handles a variety of distortions well. It is designed to be robust against pitch shifting, time stretching and tempo changes, while remaining scalable. After a query, the system returns the start time in the reference audio, and the amount of pitch shift and tempo change that has been applied. The design of the system that offers this unique combination of features is the main contribution of this research. The fingerprint itself consists of a combination of key points in a Constant-Q spectrogram. The system is evaluated on commodity hardware using a freely available reference database with fingerprints of over 30.000 songs. The results show that the system responds quickly and reliably on queries, while handling time and pitch scale modifications of up to ten percent

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Microbial community composition of transiently wetted Antarctic Dry Valley soils

During the summer months, wet (hyporheic) soils associated with ephemeral streams and lake edges in the Antarctic Dry Valleys (DVs) become hotspots of biological activity and are hypothesized to be an important source of carbon and nitrogen for arid DV soils. Recent research in the DV has focused on the geochemistry and microbial ecology of lakes and arid soils, with substantially less information being available on hyporheic soils. Here, we determined the unique properties of hyporheic microbial communities, resolved their relationship to environmental parameters and compared them to archetypal arid DV soils. Generally, pH increased and chlorophyll a concentrations decreased along transects from wet to arid soils (9.0 to ~7.0 for pH and ~0.8 to ~5 μg/cm3 for chlorophyll a, respectively). Soil water content decreased to below ~3% in the arid soils. Community fingerprinting-based principle component analyses revealed that bacterial communities formed distinct clusters specific to arid and wet soils; however, eukaryotic communities that clustered together did not have similar soil moisture content nor did they group together based on sampling location. Collectively, rRNA pyrosequencing indicated a considerably higher abundance of Cyanobacteria in wet soils and a higher abundance of Acidobacterial, Actinobacterial, Deinococcus/Thermus, Bacteroidetes, Firmicutes, Gemmatimonadetes, Nitrospira, and Planctomycetes in arid soils. The two most significant differences at the genus level were Gillisia signatures present in arid soils and chloroplast signatures related to Streptophyta that were common in wet soils. Fungal dominance was observed in arid soils and Viridiplantae were more common in wet soils. This research represents an in-depth characterization of microbial communities inhabiting wet DV soils. Results indicate that the repeated wetting of hyporheic zones has a profound impact on the bacterial and eukaryotic communities inhabiting in these areas

High-temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach

This contribution addresses two developing areas of sediment fingerprinting research. Specifically, how to improve the temporal resolution of source apportionment estimates whilst minimizing analytical costs and, secondly, how to consistently quantify all perceived uncertainties associated with the sediment mixing model procedure. This first matter is tackled by using direct X-ray fluorescence spectroscopy (XRFS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analyses of suspended particulate matter (SPM) covered filter papers in conjunction with automatic water samplers. This method enables SPM geochemistry to be quickly, accurately, inexpensively and non-destructively monitored at high-temporal resolution throughout the progression of numerous precipitation events. We then employed a Bayesian mixing model procedure to provide full characterization of spatial geochemical variability, instrument precision and residual error to yield a realistic and coherent assessment of the uncertainties associated with source apportionment estimates. Applying these methods to SPM data from the River Wensum catchment, UK, we have been able to apportion, with uncertainty, sediment contributions from eroding arable topsoils, damaged road verges and combined subsurface channel bank and agricultural field drain sources at 60- and 120-minute resolution for the duration of five precipitation events. The results presented here demonstrate how combining Bayesian mixing models with the direct spectroscopic analysis of SPM-covered filter papers can produce high-temporal resolution source apportionment estimates that can assist with the appropriate targeting of sediment pollution mitigation measures at a catchment level