Device Fingerprinting with Magnetic Induction Signals Radiated by CPU Modules

With the widespread use of smart devices, device authentication has received much attention. One popular method for device authentication is to utilize internally measured device fingerprints, such as device ID, software or hardware-based characteristics. In this article, we propose DeMiCPU , a stimulation-response-based device fingerprinting technique that relies on externally measured information, i.e., magnetic induction (MI) signals emitted from the CPU module that consists of the CPU chip and its affiliated power-supply circuits. The key insight of DeMiCPU is that hardware discrepancies essentially exist among CPU modules and thus the corresponding MI signals make promising device fingerprints, which are difficult to be modified or mimicked. We design a stimulation and a discrepancy extraction scheme and evaluate them with 90 mobile devices, including 70 laptops (among which 30 are of totally identical CPU and operating system) and 20 smartphones. The results show that DeMiCPU can achieve 99.7% precision and recall on average, and 99.8% precision and recall for the 30 identical devices, with a fingerprinting time of 0.6~s. The performance can be further improved to 99.9% with multi-round fingerprinting. In addition, we implement a prototype of DeMiCPU docker, which can effectively reduce the requirement of test points and enlarge the fingerprinting area.

A Novel Fingerprinting Technique for Data Storing and Sharing through Clouds

With the emerging growth of digital data in information systems, technology faces the challenge of knowledge prevention, ownership rights protection, security, and privacy measurement of valuable and sensitive data. On-demand availability of various data as services in a shared and automated environment has become a reality with the advent of cloud computing. The digital fingerprinting technique has been adopted as an effective solution to protect the copyright and privacy of digital properties from illegal distribution and identification of malicious traitors over the cloud. Furthermore, it is used to trace the unauthorized distribution and the user of multimedia content distributed through the cloud. In this paper, we propose a novel fingerprinting technique for the cloud environment to protect numeric attributes in relational databases for digital privacy management. The proposed solution with the novel fingerprinting scheme is robust and efficient. It can address challenges such as embedding secure data over the cloud, essential to secure relational databases. The proposed technique provides a decoding accuracy of 100%, 90%, and 40% for 10% to 30%, 40%, and 50% of deleted records.

Stable Lead Isotopic Ratios as Indicator of Urban Geochemical Processes

The study is aimed to apply the Pb isotope fingerprinting technique for tracing pollution of urban surface deposited sediment (USDS). USDS reflect changes in the geochemical conditions occurring in the environment. USDS samples were collected in residential areas with multistory buildings in Russian cities: Magnitogorsk, Nizhny Tagil, Tyumen, Ufa, and Chelyabinsk. Elements concentrations and stable Pb isotopic ratios were measured in the samples. The reconstruction of the initial geochemical baseline (IGB) relationship between potentially harmful element (PHE) Pb and conservative lithogenic element (CE) Fe was carried out for USDS sample populations in the cities. The IGB reconstruction divided USDS sample populations into the groups of ‘polluted’ and ‘unpolluted’ with Pb samples. Analysis of elements concentrations and Pb isotope ratios in the groups of USDS samples showed different trends in altering geochemical conditions for metals in the surveyed cities. The USDS is characterized by a decrease in the isotope ratios of 206Pb/204Pb and 208Pb/204Pb as a result of soil pollution by vehicles during the period of using leaded gasoline.

Improving the longevity of optically-read quantum dot physical unclonable functions

Quantum dot physically unclonable functions (QD-PUFs) provide a promising solution to the issue of counterfeiting. When quantum dots are deposited on a surface to create a token, they form a unique pattern that is unlikely to ever be reproduced in another token that is manufactured using the same process. It would also be an extreme engineering challenge to deterministically place quantum dots to create a forgery of a specific device. The degradation of the optical response of quantum dots over time, however, places a limitation on their practical usefulness. Here we report methods to minimise the degradation of photoluminescence (PL) from InP/ZnS quantum dots suspended in a polymer and demonstrate reliable authentication using a fingerprinting technique to extract a signature from PL, even after significant degradation has occurred. Using these techniques, it was found that the addition of a polylauryl methacrylate (PLMA) copolymer improved the longevity of devices. The best performing example of this was the Polystyrene-PLMA based material. From this, it is projected that 1000 bits of information could be extracted and read after a period of years, therefore providing a compelling solution to the issue of counterfeiting.

Targeted Attack through Network Fingerprinting

Nowadays, simple tools such as traceroute can be used by attackers to acquire topology knowledge remotely. Worse still, attackers can use a lightweight fingerprinting technique, based on traceroute and ping, to retrieve the routers brand, and use that knowledge to launch targeted attacks. In this paper, we show that the hardware ecosystem of network operators can greatly vary from one to another, with all potential security implications it brings. Indeed, depending on the autonomous system (AS), not all brands play the same role in terms of network connectivity and network usage (MPLS vs. standard traffic). An attacker could find an interest in targeting a specific hardware vendor in a particular AS, if known defects are present in this hardware, and if the AS relies heavily on it for forwarding its traffic.

Spatiotemporal variability in sources of suspended sediments from a Scottish river using compound-specific isotope analysis (CSIA): a nested catchment approach

<p>The River Dee is one of the major river systems in Scotland, renowned for its economically important Atlantic salmon (<em>Salmo salar</em>) population. The Tarland Burn (70 km<sup>2</sup>), an intensively managed catchment, is a significant source of nutrients and suspended sediments (SS) to the River Dee, causing degradation of its water quality. To trace the SS sources in the Tarland Burn catchment, we used compound-specific isotope analysis (CSIA) fingerprinting technique. The CSIA fingerprinting technique applied in this study involved (i) carbon isotope ratio (δ<sup>13</sup>C) measurements of plant derived long-chain fatty acids (LCFAs) extracted from source soils and from river SS as a mixture signal as input tracer values, and (ii) computation of source proportions in the mixture using an end member mixing model ‘MixSIAR’ which is based on the Bayesian approach.</p><p>Source soils were sampled from the land-use types observed in a headwater catchment (10 km<sup>2</sup>) i.e. arable, temporary grassland under arable rotation, permanent grassland, coniferous forest, heather moorland and riparian zone. SS samples were collected from the headwaters, second order streams, and also from the outlet of the Tarland Burn catchment, representing a nested sampling approach. A comparison of the two common suspended sediment collection techniques to understand the role of sampling technique and associated particle sizes resulted in no substantial difference in the tracer values. SS were sampled once every two months over a period of 14 months between May 2017 and June 2018. δ<sup>13</sup>C values of LCFAs (even homologues between C<sub>22:0</sub> - C<sub>30:0</sub>) of the SS (mixture) were within the range of source soils corresponding tracer values, confirming their conservative behaviour during transport.</p><p>Quantification of source proportions using mixing model suggested that headwater streams SS originated predominantly from permanent grasslands. They are largely located on steep topography, leading to higher hydrological connectivity and possible increased pressure from livestock. Although plantation forestry and heather moorland are prominent land-uses in the catchment, their contribution as SS sources is marginal. More intensive arable land use in the lowland areas of Tarland catchment was reflected by their high contribution to SS at the downstream locations. More intensive rainfall events during winter likely led to higher sediment fluxes from the normally less connected permanent grasslands at the catchment outlets during high flow.</p><p>Our attempt of gathering source soil information from a headwater region of a catchment and upscaling this information to model the source proportions in downstream mixtures integrating the whole catchment was successful, however uncertainties increased for the downstream results</p>