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 paper, 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 99.1% precision and recall on average, and 98.6% precision and recall for the 30 identical devices, with a fingerprinting time of 0.6 s. In addition, the performance can be further improved to 99.9% with multi-round fingerprinting.

Human activity recognition (HAR) using wearable sensors is becoming widely used in a large range of applications. Magnetic induction-based human activity recognition system (MI-HAR) is a new wearable-based HAR system that captures human motions by variations in the magnetic induction (MI) signals received from transmitter coils placed around the human body. In this work, we focused on the performance analysis of the MI-HAR system using experimental measurements. The main aim of this study is to show the sensitivity of MI signals to spatial translation and rotations of MI coils and verify the capability of the MI-HAR system in detecting relative motion between coils. Moreover, we compare the simulated MI-motion data generated by the MI system model with the data measured by vector network analyzer (VNA).

Magnetic induction detection technology is a non-contact and non-invasive electrical impedance detection technology. Multi-frequency synchronous detection can simultaneously obtain the impedance information of the tested object at different frequencies. Firstly, the principle of multi-frequency synchronous excitation and detection of magnetic induction signal are studied. Five-frequency excitation signal is synthesized based on Walsh function. Secondly, the performance of synthesized multi-frequency synchronous detection is analyzed, and a synthesized multi-frequency magnetic induction signal synchronous detection system is designed. Finally, the detection experiments of NaCl solution with different conductivities are carried out by synthesizing five-frequency excitation signal and synchronous detection system. The results show that the measurement results of five main harmonics of synthesized five-frequency excitation signal have good linearity. It provides an excitation-detection method for multi-frequency synchronous detection of magnetic induction signal.

The Magnetic Induction (MI) communication techniques have enabled or enhanced many wireless applications in the indoor environments where line-of-sight (LOS) links usually do not exist. The position information of each wireless device in such complex environment can also be derived by the same MI systems without additional hardware or infrastructure. However, while MI signals can penetrate most transmission media without significant attenuation and phase shifting, the conductive objects in the indoor environment (e.g., metallic pipelines, beams, and human bodies) can still dramatically influence the MI signals, which can cause significant estimation errors in the MI-based indoor localization. To date, no analysis/solution has been provided to address such problem. In this paper, an environment-aware indoor localization mechanism is proposed for MI-based wireless networks in complex non-LOS environments without preinstalled infrastructures. First, the influence of conductive objects on the MI-based wireless network in indoor environment is investigated. Then based on the influence analysis, a joint device localization and conductive-object tomography algorithm is developed to estimate the position of each wireless devices as well as distribution of objects. The simulation evaluation shows the proposed mechanism can accurately localize each device in a MI-based networks in a complex floor plan with multiple conductive walls and obstructions.

The magnetic field is less attenuated across the air-sea interface because that air and seawater have similar magnetic permeability. This property is used for magnetic induction (MI) communication from air to sea, with high efficiency and superiority. This paper introduces a test of magnetic induction (MI) communication from the air to sea water. First, the relationship between magnetic field strength and various factors, such as distance and signal frequency, is simulated and the numeral results indicate that the frequency and the transmitting magnetic moment have great influence on the MI strength, therefore on the penetrated depth. Secondly, a MI system for magnetic induction (MI) communication is developed and is used to carry out a MI test. The MI signals modulated by frequency-shift-keying (FSK) are transmitted by a transmitting antenna located in the air and received by a submerged receiver with a three-component flux gate sensor. Error free data communication from a height of 2 m above the surface to a depth of 35 m below the surface was achieved after signal processing.

Aiming at the low effect and intelligence of traditional pre-concentration methods for screening low-grade magnetite ore, a method based on convolutional neural network (CNN) is proposed. According to the simulation result of COMSOL Multiphysics for magnetite ore, the magnetic induction signals acquisition system is built and the signal acquisition method is designed. The magnetic induction signals of 1200 magnetite ores are collected and converted into two-dimensional signals that CNN is good at processing through sample preparation. The network model is constructed, and the parameters of the model is optimized by orthogonal experiment design. The optimized model is trained and tested based on the experimental data. The results show that the CNN model can effectively extract the magnetic induction signal characteristics of magnetite ore, and the recognition accuracy rate is as high as 87.5 %.

This paper designs a novel geometry-conformal antenna for Magnetic Induction (MI)-based subsea wireless communications for autonomous underwater vehicles (AUV). The designed tri-directional antennas can be wrapped directly on the surface of AUVs, such that the AUVs fluid dynamics are well maintained to ensure power efficiency of the vehicles. In addition, ferrite materials are added between the MI antenna and the metallic body surface of the AUVs to overcome the shielding effect and enhance the MI signal strength. The designed MI communication system is implemented in hardware and the effectiveness of the geometry-conformal MI antenna is demonstrated through COMSOL simulations and lab experiments.

Environment-aware localization for wireless sensor networks using magnetic induction

In response to the difficulties and poor timeliness in detecting feeding metallic foreign objects during high-yield continuous crushing operations in coal mines, this paper proposes a new method for detecting metallic foreign objects, combining pulsed eddy current testing with the Truncated Region Eigenfunction Expansion (TREE) method. This method is suitable for the harsh working conditions in coal mine crushing stations, which include high dust, strong vibration, strong electromagnetic interference, and low temperatures in winter. A model of the eddy current field of feeding metallic foreign objects in the truncated region is established using a coaxial excitation and receiving coil with a Hall sensor. The full-cycle time-domain analytical solution for the induced voltage and magnetic induction intensity of the reflective field under practical square wave signals is obtained. Simulation and experimental results show that the effective time range, peak value, and time to peak of the received voltage and magnetic induction signals can be used to classify and identify the size, thickness, conductivity, and magnetic permeability of feeding metallic foreign objects. Experimental results meet the actual needs for removing feeding metallic foreign objects in coal mine sites. This provides core technical support for the establishment of a predictive fault diagnosis system for crushing equipment.

The discovery of Europa's subsurface ocean has spawned a strong desire by the planetary community to return and assess the ocean's habitability using the magnetic induction signal that Europa generates. NASA has since formulated and developed the Europa Clipper mission with that same goal, anticipating its arrival in the Jovian system in the early 2030s. In parallel, ESA has developed the JUpiter Icy moons Explorer mission to further investigate the interior of Ganymede and other Jovian moons, scheduled to arrive approximately one year later. As a result, extensive work has now been devoted to developing and refining methods to analyse magnetic induction measurements with the goal of characterizing oceans within icy moons, including those in the Neptune and Uranus systems, which are ideal laboratories for such investigations. We present one such method, involving a distance-based inverse and forward modelling approach that leverages self-consistent interior models used to infer ocean and ice-shell properties of various moons that respond inductively to the dynamic magnetic environments in which they reside. We demonstrate the method on a hypothetical ocean within Umbriel, showing the ocean thickness and conductivity constraints that can be inferred from a Monte Carlo error analysis using a three-flyby mission concept.This article is part of the theme issue 'Magnetometric remote sensing of Earth and planetary oceans'.

The urgent need to protect user privacy and security has emerged as the World Wide Web has become an increasingly necessary part of daily life. Browser fingerprinting is a very interesting topic in the industry of technology security. New technology will always raise new security issues and browser fingerprinting will undoubtedly follow the same process. It has become one of the most popular topics in online privacy because, to date, there is still no exact solution as to how to stop it entirely. The majority of solutions just aim to reduce the likelihood of obtaining a browser fingerprint. Research on browser fingerprinting is unquestionably required since it is essential to educate users, developers, policymakers, and law enforcement about it so that they can make strategic choices based on knowledge. Browser fingerprinting must be recognised in order to defend against privacy problems. A browser fingerprint is described as data gathered by the receiving server to identify a distant device, and it is different from cookies. Websites frequently utilize browser fingerprinting to obtain information about the type and version of the browser, as well as the operating system, and other current settings. It has been known that even when cookies are disabled, fingerprints can be used to fully or partially identify users or devices. In this communication paper, a new insight into the challenge of browser fingerprint is encouraged as a new venture. Thus, the initial way to truly understand the browser fingerprint is the need to collect browser fingerprints. In this work, the process of data collection for browser fingerprinting through scripting, to offer a complete all-in-one fingerprinting test suite, has been thoughtfully divided into appropriate sections and grouped with key information to be carried out. The objective is to gather fingerprint data with no personal identification information and make it an open source of raw datasets in the industry for any future research purposes. To our best knowledge, there are no open datasets made available for browser fingerprints in the research field. The dataset will be widely accessible by anyone interested in obtaining those data. The dataset collected will be very raw and will be in the form of a text file. Thus, the main contribution of this work is to share an open dataset of browser fingerprints along with its collection methodology.

Human motion tracking has broad applicability, and currently, various approaches are available for its deployment. Wearable sensors and vision-based methods are among the most commonly used techniques that still face challenges in terms of accuracy, privacy, coverage, and cost. The magnetic induction (MI) system is a new approach based on inductive coupling, representing motions via variation in the MI signals. In this work, we present the performance analysis of the MI system and compare its accuracy with a high frequency (HF) radio frequency identification (RFID) system using experimental measurements in 3D motion tracking.

Wireless underground sensor networks enable many applications, such as mine and tunnel disaster prevention, oil upstream monitoring, earthquake prediction and landslide detection, and intelligent farming and irrigation among many others. Most applications are location-dependent, so they require precise sensor positions. However, classical localization solutions based on the propagation properties of electromagnetic waves do not function well in underground environments. This paper proposes a magnetic induction (MI)-based localization that accurately and efficiently locates randomly deployed sensors in underground environments by leveraging the multipath fading free nature of MI signals. Specifically, the MI-based localization framework is first proposed based on underground MI channel modeling with additive white Gaussian noise, the designated error function, and semidefinite programming relaxation. Next, this paper proposes a two-step positioning mechanism for obtaining fast and accurate localization results by: first, developing the fast-initial positioning through an alternating direction augmented Lagrangian method for rough sensor locations within a short processing time, and then proposing fine-grained positioning for performing powerful search for optimal location estimations via the conjugate gradient algorithm. Simulations confirm that our solution yields accurate sensor locations with both low and high noise and reveals the fundamental impact of underground environments on the localization performance.

The Magnetic Induction (MI)-based communication techniques enable the applications of wireless sensor networks (WSNs) in complex environments, such as underground and underwater environments. However, the complex environments usually consist of many conductive objects that can significantly enhance or block the MI signals. Efficient communications can be achieved if we can smartly utilize the positive factors and prevent the channel from the negative factors by selecting the optimal operating frequency and routing relays in the networks. However, this strategy cannot be realized if the environment is unknown to us. To address such a problem, we propose an environment-aware method for WSNs applications in the complex environments. In this paper, a channel model is first developed by considering the complex environments and the positive/negative factors are analyzed based on the model. Then, based on the training data obtained by the handshake between transceivers, the Kernel method is used to learn the positive/negative factors in the environment. Finally, an example of environment-aware routing protocol is presented to show that the proposed environment-aware method can be used to significantly improve the efficiency of the networks by optimally selecting the channel and the routing relays.

The authors examine the theoretical and practical problems connected with determining the concept and the possibilities of using digital fingerprints of computer devices, found in the information-communication networks, in court proceedings. The authors present key goals of identifying computer devices by their digital fingerprints based on selected key and specific features from the standpoint of digital criminalistics. It is stated that a device fingerprint is a digital trace formed as an arbitrary value of parameters, the configurations of software and hardware of a specific computer device. It is virtually impossible to falsify a digital fingerprint. Key areas of using device fingerprints are examined. The list of parameters used for identifying a digital device by its computer fingerprint in Russian and foreign practice is analyzed. In Russian legal practice, a digital fingerprint is formed by the identifiers of the device’s hardware, operation system’s version, browser’s version, and others. The authors point out that there are two types of device fingerprints: browser fingerprint (helps identify both desktop and mobile devices) and mobile device’s fingerprint. Due to a complex formation mechanism of this type of fingerprints connected with its unique character and frequency of changes in its features, the authors describe the circumstances that should be taken into consideration when obtaining it. They present some methods for improving the effectiveness of this work and further identification of a specific device by its digital fingerprints, as well as a system of criminalistic situations of using device fingerprints. In conclusion it is stated that device fingerprints have a considerable identification potential that could be used not only within the framework of anti-fraud systems, but also in preventing and investigating computer-related incidents and cybercrimes, user de-anonymization, as well as protection of copyright and development of targeted advertising.