Thematische Bibliographien / Device fingerprint

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Auswahl der wissenschaftlichen Literatur zum Thema „Device fingerprint“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 2. Februar 2022

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Zeitschriftenartikel zum Thema "Device fingerprint"

Arai, Fumihito, und Toshio Fukuda. „Fingerprint Image Sensing Using Micromechanical Key and Extraction Algorithm for Sensed Fingerprint Image“. Journal of Robotics and Mechatronics 13, Nr. 5 (20.10.2001): 458–63. http://dx.doi.org/10.20965/jrm.2001.p0458.

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A novel fingerprint image sensing device, which combines the ID bar code with fingerprints, has been proposed and developed. Due to the use of ID bar codes, higher security and less recognition time can be ensured. The device has been fabricated using new micromachining. The sensed image with this sensing device consists of both fingerprint patterns and ID bar lines. The corresponding extraction algorithm for the sensed fingerprint image has also been researched and developed. The algorithm extracts an ID bar image and a pure fingerprint image without ID bar lines from the single sensed image. The ID code can be extracted from the ID bar image, and the fingerprint image is processed to realize fingerprint identification.

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Gabryel, Marcin, Konrad Grzanek und Yoichi Hayashi. „Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic“. Journal of Artificial Intelligence and Soft Computing Research 10, Nr. 4 (01.10.2020): 243–53. http://dx.doi.org/10.2478/jaiscr-2020-0016.

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AbstractWeb-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.

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Kurtz, Andreas, Hugo Gascon, Tobias Becker, Konrad Rieck und Felix Freiling. „Fingerprinting Mobile Devices Using Personalized Configurations“. Proceedings on Privacy Enhancing Technologies 2016, Nr. 1 (01.01.2016): 4–19. http://dx.doi.org/10.1515/popets-2015-0027.

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Abstract Recently, Apple removed access to various device hardware identifiers that were frequently misused by iOS third-party apps to track users. We are, therefore, now studying the extent to which users of smartphones can still be uniquely identified simply through their personalized device configurations. Using Apple’s iOS as an example, we show how a device fingerprint can be computed using 29 different configuration features. These features can be queried from arbitrary thirdparty apps via the official SDK. Experimental evaluations based on almost 13,000 fingerprints from approximately 8,000 different real-world devices show that (1) all fingerprints are unique and distinguishable; and (2) utilizing a supervised learning approach allows returning users or their devices to be recognized with a total accuracy of 97% over time

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Szweda, Roy. „Holographic fingerprint security device“. Network Security 1997, Nr. 7 (Juli 1997): 7. http://dx.doi.org/10.1016/s1353-4858(97)89874-1.

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Szczepański, Tomasz, Urszula Więckiewicz, Barbara Konior und Patryk Pucułek. „Vacuum metal deposition (VMD) – characteristics of the method“. Issues of Forensic Science 308 (2020): 40–46. http://dx.doi.org/10.34836/pk.2020.308.1.

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Vacuum metal deposition was introduced in the process of visualization of latent fingermarks already in the 20th century. However, due to the requirement of using specialist equipment to ensure appropriate conditions for the development process, which would have generated significant costs, the method was not available in Poland. Technological developments, however, made it possible to create compact devices with smaller dimensions and lower parameters, which, nowadays, can be used in virtually every forensic fingerprint identification laboratory. The article describes the theoretical basis of the process of developing fingerprints by means of the vacuum metal deposition method with the use of sequential deposition of gold, zinc and silver on the tested surfaces. The device used for the VMD method is also presented along with sample effects of treating fingerprints on various surfaces, including registration in the range of reflected infrared light and with recovering marks on lifting foils. The final part deals with the issue of accrediting forensic service providers performing fingerprint examinations, including problems related to the implementation of vacuum metal deposition in the range of accredited fingerprint visualization techniques.

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Meretukov, Gaysa Mosovich, Vitaliy Viktorovich Pomazanov und Sergei Ivanovich Gritsaev. „Some issues of using iodine vapour and ozone-air mixture in law-enforcement intelligence operations for finding fingerprints for the purpose of crimes investigation“. Полицейская и следственная деятельность, Nr. 1 (Januar 2020): 21–25. http://dx.doi.org/10.25136/2409-7810.2020.1.31344.

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The object of research in this article is the activities of a person who had left fingerprints and the activities of a law-enforcement officer using a mobile device generating iodine vapour aimed at the detection of a person suspected of having committed an offence, and the treatment of the detected fingerprint with ozone-air mixture which would decolourise it. A complex of a mobile device for treating fingerprints with iodine vapour, photographic and video equipment, and an ozonator will increase the effectiveness of investigative activities of p[erative units during law-enforcement intelligence operations. The research is based on the dialectical method of cognition, general scientific methods: observation, experiment, comparison, description, measurement, etc., and general logical methods and research techniques. The scientific novelty consists in the creation of a way to use iodine vapour and ozone-air mixture to treat fingerprints, which would help to get the suspect’s fingerprints quickly, without disarranging the evidences, without any traces after the registration of a fingerprint, and without the seizure of the object bearing the fingerprint. Such a mechanism used during law-enforcement intelligence operations would help identify the person suspected of having committed an offence.   

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Drake, Marvin D. „Waveguide hologram fingerprint entry device“. Optical Engineering 35, Nr. 9 (01.09.1996): 2499. http://dx.doi.org/10.1117/1.600843.

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Lalovic, Komlen, Milan Milosavljevic, Ivan Tot und Nemanja Macek. „Device for biometric verification of maternity“. Serbian Journal of Electrical Engineering 12, Nr. 3 (2015): 293–302. http://dx.doi.org/10.2298/sjee1503293l.

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Biometry is the scientific discipline and technology that measures and analyzes physiological or behavioral characteristics of people and is widely deployed in modern society security systems. Device for biometric identification of maternity is a dual fingerprint scanner that acquires fingerprint templates of the mother and the child at the very moment of birth, generates unique ID reference, and further guarantees mother-child relationship with that reference. Technical issue that is solved with this work and the proposed device is scanning, processing, and storing encrypted biometric templates with a goal to provide a 100% guarantee maternity for each new born child. Scanning the fingerprints of both mother and the child simultaneously, at moment of birth, and pairing them with unique ID reference removes potential fears occurring from hospital negligence to malicious activities, while the data encryption raises the whole process to the highest level of security and confidentiality. The main contribution of the device that removes the fear that almost every mother has in this period as it provides an answer to the question: ?Is this my baby?? with a 100% guarantee ?It?s certainly yours!?

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Subpratatsavee, Puchong, und Narinwat Pubpruankun. „A Design and Implementation of Attendance System Using Smallest Wireless Fingerprint with Arduino Yún Embedded Board“. Applied Mechanics and Materials 752-753 (April 2015): 1057–61. http://dx.doi.org/10.4028/www.scientific.net/amm.752-753.1057.

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The purpose of this research is a design and implementation of a smallest wireless fingerprint device based on Arduino Yún board. This system applies for using in class attendance checking in school or university. The exper-imental of class attendance using wireless fingerprint in this research is examinations in Kasetsart University Siracha Campus, Thailand for case study. All hardware environments of this system-using platform of Arduino Yún board and Ubuntu OS apply with computer program like MySQL, JavaScript, Apache server, and wireless network for building an embedded fingerprint authentication system. Design of a new smallest wireless fingerprint in this research is small and easy to use. It has a LCD screen for show the result of identifying after users scan they fingerprint on it. This system is applying to use in class attendance. When student come in a class he or she scan his fingerprint on our device for checking and authentications. After device receive data from his fingerprint then device send this data to server using wireless network. When server get the data by device from network already, it will send this data to fingerprint authentications program for loading and checking data of fingerprint in database. If his fingerprint data is match one of data in database the server will be sent the name and information of matching fingerprint data back to LCD screen of device and show valid text on the screen and update the status of fingerprint owner to attendance system. If his fingerprint not math in all of data in database, it invalid. This research help an instructor for reduce time for checking student attendance; solve the problem about some student absent but his or her friend fake attendance for him. And in the future this device will be replacing the student ID card

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COLI, PIETRO, GIAN LUCA MARCIALIS und FABIO ROLI. „FINGERPRINT SILICON REPLICAS: STATIC AND DYNAMIC FEATURES FOR VITALITY DETECTION USING AN OPTICAL CAPTURE DEVICE“. International Journal of Image and Graphics 08, Nr. 04 (Oktober 2008): 495–512. http://dx.doi.org/10.1142/s0219467808003209.

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The automatic vitality detection of a fingerprint has become an important issue in personal verification systems based on this biometric. It has been shown that fake fingerprints made using materials like gelatine or silicon can deceive commonly used sensors. Recently, the extraction of vitality features from fingerprint images has been proposed to address this problem. Among others, static and dynamic features have been separately studied so far, thus their respective merits are not yet clear; especially because reported results were often obtained with different sensors and using small data sets which could have obscured relative merits, due to the potential small sample-size issues. In this paper, we compare some static and dynamic features by experiments on a larger data set and using the same optical sensor for the extraction of both feature sets. We dealt with fingerprint stamps made using liquid silicon rubber. Reported results show the relative merits of static and dynamic features and the performance improvement achievable by using such features together.

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Dissertationen zum Thema "Device fingerprint"

Baral, Prashant. „DEVICE IDENTIFICATION USING DEVICE FINGERPRINT AND DEEP LEARNING“. OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2866.

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Billions of devices are connected to networks, which share data between them or with the processing device. With the advent of technology, cost-effective hardware can be interface with sensors and can act as an IoT device; This provides them with digital intelligence and communicates by connecting to Network. Like in other network devices, security is of utmost importance in IoT as well. Security in IoT is challenging than in traditional devices because of lower computing power, lower computing resources, small battery, and many IoT devices deployed. Research in cryptography, a lightweight communication protocol is focused. However, there is no substantial research effort for the identity of IoT devices which is the fundamental factor in Security. Traditional devices use MAC address, IP Address, and IMEI for physical identity but these parameters for physical device identification are prone to sniffing and spoofing; Thus, is not that reliable. Relying on these parameters for the security of IoT devices compromises the critical information and IoT device itself.The thesis utilizes the information from the packets used for communication in networks for device identification. We use IAT (Inter Arrival Time) and RTT (Round Trip Time) of packets at the router side to uniquely identify the IoT device. IAT is the time difference between two packets received at Router and RTT is the time from request to response packets at Router. IAT and RTT are different for a device as it depends on the hardware and software of a device. We plot the graph of IAT and RTT separately and use deep learning as a tool to identify a device. We use different deep learning algorithms for identification. We use CNN and LSTM for IAT graphs and RTT graphs. Both deep Learning algorithms achieve good accuracy in the classification of a device using both parameters (IAT and RTT) but to verify using the publicly available dataset, we achieved 97% accuracy in classifying using IAT using CNN in the testing dataset. Using CNN+LSTM we achieve an accuracy of 91.45% in the classification of a device using IAT as a parameter in the testing dataset.

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Vondráček, Tomáš. „Získávání informací o uživatelích na webových stránkách“. Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445554.

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The aim of the diploma thesis is to map the information provided by web browsers, which can be used in practice to identify users on websites. The work focuses on obtaining and subsequent analysis of information about devices, browsers and side effects caused by web extensions that mask the identity of users. The acquisition of information is realized by a designed and implemented library in the TypeScript language, which was deployed on 4 commercial websites. The analysis of the obtained information is carried out after a month of operation of the library and focuses on the degree of information obtained, the speed of obtaining information and the stability of information. The dataset shows that up to 94 % of potentially different users have a unique combination of information. The main contribution of this work lies in the created library, design of new methods of obtaining information, optimization of existing methods and the determination of quality and poor quality information based on their level of information, speed of acquisition and stability over time.

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Derakhshani, Reza. „Determination of vitality from a non-invasive biomedical measurement for use in integrated biometric devices“. Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=1035.

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Thesis (M.S.)--West Virginia University, 1999.
Title from document title page. Document formatted into pages; contains x, 126 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. [72]-75).

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Sjöbro, Linus. „Automatic retrieval of data for industrial machines with handheld devices : Positioning in indoor environments using iBeacons“. Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42742.

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Positioning of mobile phones or other handheld devices in indoor environments is hard because it’s often not possible to retrieve a GPS-signal. Therefore, other techniques need to be used for this. Despite the difficulties with indoor positioning, the Swedish mining company LKAB want to do exactly this in their processing plants. LKAB has developed an Apple iPhone mobile application to maintain real-time process data and documents for their machines. To retrieve the information an OCR code need to be manually scanned with the application. Instead of manually scanning these codes, LKAB want to develop an Indoor Positioning System that can automatically locate handheld devices in their production plants. This thesis aimed to create a proof of concept Apple iOS application that can position devices without GPS-signals. In the system developed Bluetooth Low Energy iBeacons is used to transmit data to the application. From this data Received Signal Strength Indication values is collected and sent off to a server that transform the values into positioning fingerprints. These fingerprints are used together with the classification algorithms K-Nearest Neighbour to determine in which, on pre-hand created, group the user is located. In these created groups there is a defined set of machines that is being presented back to the user. Test results conducted with the proof of concept application shows that the implemented system works and gives a positioning accuracy of up to 75%.

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Radspinner, David Andrew 1965. „Developments in atomic analysis and imaging utilizing scientific charge-transfer devices: Axial viewing of the inductively coupled plasma, advanced hollow cathode designs, and latent fingerprint imaging“. Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/282524.

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This document describes the development of spectroscopic techniques which benefit from the use of charge-transfer devices. Both charge-coupled devices (CCD's) and charge-injection devices (CID's) are used in the techniques presented here such as atomic emission spectroscopy and latent fingerprint imaging. The use of a CID echelle system for axial viewing of the inductively coupled plasma (ICP) demonstrates the enhancement in sensitivity that can be obtained over tangential viewing. More importantly though, are the advantages afforded by simultaneous multi-element detection. Axial viewing of the ICP has shown to not only improve upon the detection limits of several metals by, in some cases, a half order of magnitude, but also to increase the amount of light collected and thus reduce the time of analysis. Along with this, the effect of interferences upon the detection of various metals is, at worst, equivalent to that of an ICP with tangential viewing. Further enhancement of sensitivity in atomic analysis can be achieved by atomic fluorescence with an ICP. Although in the past, hollow cathode lamps have proven to be insufficient, advanced designs of hollow cathode lamps presented here have demonstrated an increase in the intensity of lines of copper best suited for use in ICP atomic fluorescence. Lastly, a latent fingerprint has been imaged with the use of a scientific CCD and a flashlight where in the past such a technique was accomplished with high power lasers. By using a CCD, the immediate digitization of information combined with the sensitivity and image processing capabilities offer a portable means by which to image latent fingerprints on poor surfaces.

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Nishibe, Caio Arce. „Central de confrontos para um sistema automático de identificação biométrica: uma abordagem de implementação escalável“. Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/3142.

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Com a popularização do uso da biometria, determinar a identidade de um indivíduo é uma atividade cada vez mais comum em diversos contextos: controle de acesso físico e lógico, controle de fronteiras, identificações criminais e forenses, pagamentos. Sendo assim, existe uma demanda crescente por Sistemas Automáticos de Identificação Biométrica (ABIS) cada vez mais rápidos, com elevada acurácia e que possam operar com um grande volume de dados. Este trabalho apresenta uma abordagem de implementação de uma central de confrontos para um ABIS de grande escala utilizando um framework de computação em memória. Foram realizados experimentos em uma base de dados real com mais de 50 milhões de impressões digitais em um cluster com até 16 nós. Os resultados mostraram a escalabilidade da solução proposta e a capacidade de operar em grandes bases de dados.
With the popularization of biometrics, personal identification is an increasingly common activity in several contexts: physical and logical access control, border control, criminal and forensic identification, payments. Thus, there is a growing demand for faster and accurate Automatic Biometric Identification Systems (ABIS) capable to handle a large volume of biometric data. This work presents an approach to implement a scalable cluster-based matching platform for a large-scale ABIS using an in-memory computing framework. We have conducted some experiments that involved a database with more than 50 million captured fingerprints, in a cluster up to 16 nodes. The results have shown the scalability of the proposed solution and the capability to handle a large biometric database.

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Kai-Jen, Chang, und 張凱然. „Design of Optical System in Fingerprint Recognition Device“. Thesis, 2004. http://ndltd.ncl.edu.tw/handle/76171355540134101243.

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碩士
輔仁大學
物理學系
92
Fingerprint Recognition System is an important event of analyze and develop in recent years. Including of Solid-State Fingerprint Sensor and Indirect-Contact Optical Collection are the two types usually seen. This article belongs to the second kind of Indirect-Contact Optical Collection. It takes images by using Optical System to contact Fingerprints. In order to let the irradiated area on fingers is big enough ,we choose Kingbright L-7676CSYC SUPER BRIGHT YELLOW which light source Viewing Angle (2θ/2) is 70°. It’s Dominate Wavelength is 588 nm and it’s Peak Wavelength is 590 nm. Then the sensor CCD is choose to use SONY ICX259AL. We use regular triangle prism when design optical system, so as to calculate the light incidence and outgoing one, and we choose plastic for material to reduce the cost. At our designing ,we try to detect the scattering light from the range of normal direction to 26 degrees by the normal on the detecting surface of a prism as after the incident light go through that prism with once refraction and once reflection to on the detecting surface . Then focus that what we get from the touching surface on the detector after that we got experience once reflection and refraction again inside the prism used. In the aspect of lens imaging, we use ZEMAX formula to calculate lens’ curvature and Aspherical parameter.

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Sang, Mao-Yang, und 桑茂洋. „Fingerprint Assisted Resource Allocation for Device-to-Device Communication Underlaying Cellular Networks“. Thesis, 2014. http://ndltd.ncl.edu.tw/handle/e7xhak.

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碩士
國立中正大學
通訊工程研究所
102
Device-to-Device (D2D) communication is a brand-new fashion that allows mobile station communicating directly with each other using existing licensed band in cellular networks. D2D communication is considered as the technology to more efficiently utilize the licensed band for the next evolution in mobile communication networks. Most licensed band allocation methods were based on the assumption of eNodeB knowing measured channel gain of every links between all mobile stations. However, the measurement would be a huge expense when there were many mobile stations in a cell. Aiming the obstacle, this thesis proposes a fingerprint technique for eNodeB to estimate the degree of interference between mobile stations without measuring every channel gain, and therefore to determine which radio resource block can be reused. This fingerprint technique can significantly reduce the number of channel gain measurement between mobile stations. Moreover, by simulating D2D communication in LTE network, this study shows that fingerprint assisted resource allocation does not only more effectively raise system sum rate than random allocation do, but also performs closer to a sub-optimal solution derived by a costly greedy algorithm that depends on the knowledge of every channel gain.

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Chang, Kai-Shun, und 張凱舜. „Architecture Design and Implementation of Wireless Fingerprint Reader Device“. Thesis, 2000. http://ndltd.ncl.edu.tw/handle/65892221139558843040.

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碩士
國立清華大學
電機工程學系
88
Biometrics of human begins are widely used as personal identification in order to substitute for the defects of passwords, personal identification numbers, credit cards, keys, …etc. The use of biometrics on security systems enjoys the superiority of high security. Among all biometric features, fingerprint verification is the most reliable technology. The procedures of a fingerprint identification system can be divided into three stages that are fingerprint capture, image pre-processing and feature matching. The performance of a fingerprint identification system may be influenced by the procedures of each stage. In the past, infrared technology was merely used on military applications. Recently, applications of infrared technology are extended to other areas such as data communications and medical applications. Infrared data communications enjoy much merit of the high data privacy, low complexity and low cost over RF. Till now, the widespread use of infrared technology on computer, communications and consumer electronic products provide users with tools for universal connectivity. Therefore, we design and develop a wireless fingerprint identification and authentication system through the combination of fingerprint verification and infrared data communication technology. And we aim at the wireless communication of our system to propose some requirements for transmission speeds, capabilities of data link control, multiplexing, …etc. For that, we refer to a suit of standards defined by Infrared Data Association for development of the communication part. Further, in order to provide the system with entity authenticity for preventing unauthorized access, we use the challenge-response protocol to design a secure link connection protocol. In addition, we have a discussion with certain concern for the design and hardware implementation of wireless fingerprint reader that plays an important role in our proposed system. With these efforts, we start to concern application areas of our proposed system. The chief application areas are physical access control and authentication. And the main focus of this work is to design an access system on the basis of our system architecture. After that, we will implement it as a real-work access system for the proof of feasibility and practicability.

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WU, CHIAO-YU, und 鄔喬妤. „The Application of Fingerprint Recognition on Mobile Device in Internet of Vehicles“. Thesis, 2016. http://ndltd.ncl.edu.tw/handle/5b5xm4.

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碩士
國立臺灣科技大學
電機工程系
105
With the rapid progress of mobile wireless communication technology, cell phone is not only a handheld mobile device but also a platform for data exchanging. Therefore, it is a vital issue how to authenticate identity while dealing with large amounts of data. This thesis focuses on the biometric authentication and proposed a “Taxi Safety Certification System”. Through wireless communication, this study transfers the fingerprints to machine without biometrics identification technology and authenticate user identity on device. There is no need to equip biometrics identification system for safety and convenience benefits. The main process of the proposed system focuses on the identity authentication of the driver. It allows drivers to steer a taxi via smartphones, which includes both locking and unlocking the vehicle. This system is used to unlock the car door by near field communication technology which combines car keys with the fingerprint identification system. Even though the smartphone is lost, the car isn't under the risk of theft. From a practical point of view, compared with remote key, the action speed and operating time in report of passing fingerprint to lock/unlock car door by NFC are both slightly longer, but in terms of effect, it can get more control methods and more freedom with safety guaranteed.

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Bücher zum Thema "Device fingerprint"

Zofka, Adam, Maria Chrysochoou, Iliya Yut, Chad Johnston, Montgomery Shaw, Shih-Po Sun, James Mahoney, Stuart Farquharson und Michael Donahue. Evaluating Applications of Field Spectroscopy Devices to Fingerprint Commonly Used Construction Materials. Washington, D.C.: Transportation Research Board, 2013. http://dx.doi.org/10.17226/22770.

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Chrysochoou, Maria, Chad Johnston und Iliya Yut. Evaluating Applications of Field Spectroscopy Devices to Fingerprint Commonly Used Construction Materials (Phase IVâ€"Implementation). Washington, D.C.: Transportation Research Board, 2014. http://dx.doi.org/10.17226/22308.

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Buchteile zum Thema "Device fingerprint"

Zhang, David, und Guangming Lu. „3D Fingerprint Acquisition Device“. In 3D Biometrics, 171–94. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7400-5_10.

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Cheng, Xiaochun, Andreas Pitziolis und Aboubaker Lasebae. „Implementing Fingerprint Recognition on One-Time Password Device to Enhance User Authentication“. In Cyberspace Safety and Security, 448–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37352-8_39.

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Khan, Muhammad Khurram, Saru Kumari, Mridul K. Gupta und Fahad T. Bin Muhaya. „Cryptanalysis of Truong et al.’s Fingerprint Biometric Remote Authentication Scheme Using Mobile Device“. In Advances in Brain Inspired Cognitive Systems, 271–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38786-9_31.

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Valussi, Silvia, und Andreas Manz. „Electric Field Assisted Extraction and Focusing of Fingerprint Residues by Means of A Microfluidic Device“. In Micro Total Analysis Systems 2002, 865–67. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0504-3_88.

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Ciere, Michael, Carlos Gañán und Michel van Eeten. „Partial Device Fingerprints“. In Machine Learning and Knowledge Discovery in Databases, 222–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_14.

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Chen, Dajiang, Xufei Mao, Zhen Qin, Weiyi Wang, Xiang-Yang Li und Zhiguang Qin. „Wireless Device Authentication Using Acoustic Hardware Fingerprints“. In Big Data Computing and Communications, 193–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22047-5_16.

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Sigg, Stephan, Matthias Budde, Yusheng Ji und Michael Beigl. „Entropy of Audio Fingerprints for Unobtrusive Device Authentication“. In Modeling and Using Context, 296–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24279-3_31.

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Anushka Swarup, Kottapalli Dheeraj und Adesh Kumar. „Fingerprint-Based Attendance System Using MATLAB“. In Proceeding of International Conference on Intelligent Communication, Control and Devices, 999–1004. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1708-7_117.

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Tuveri, Pierliugi, L. Ghiani, Mikel Zurutuza, V. Mura und G. L. Marcialis. „Interoperability Among Capture Devices for Fingerprint Presentation Attacks Detection“. In Handbook of Biometric Anti-Spoofing, 71–108. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92627-8_4.

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Prabhu, Pravin, Ameen Akel, Laura M. Grupp, Wing-Kei S. Yu, G. Edward Suh, Edwin Kan und Steven Swanson. „Extracting Device Fingerprints from Flash Memory by Exploiting Physical Variations“. In Trust and Trustworthy Computing, 188–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21599-5_14.

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Konferenzberichte zum Thema "Device fingerprint"

Aneja, Sandhya, Nagender Aneja und Md Shohidul Islam. „IoT Device Fingerprint using Deep Learning“. In 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS). IEEE, 2018. http://dx.doi.org/10.1109/iotais.2018.8600824.

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Yin, Xinming, Zhengliang Hu, Guoliang Chen, Haiye Huang und Zhiwei Cao. „Research and Application of Device Fingerprint“. In 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/mecae-17.2017.87.

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Sadasivuni, Kishor Kumar, Mohammad Talal Houkan, Mohammad Saleh Taha und John-John Cabibihan. „Anti-spoofing device for biometric fingerprint scanners“. In 2017 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2017. http://dx.doi.org/10.1109/icma.2017.8015898.

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Ostberg, Anna, Mohamed Sheik-Nainar und Nada Matic. „Using a Mobile Device Fingerprint Sensor as a Gestural Input Device“. In CHI'16: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2851581.2892419.

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Xue, Juntao, Shiming Wang und Jie Shi. „Serial fingerprint device driver development based on EFI“. In Mechanical Engineering and Information Technology (EMEIT). IEEE, 2011. http://dx.doi.org/10.1109/emeit.2011.6022929.

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Deelaka Ranasinghe, R. M. Nipuna, und Guan Zhen Yu. „RFID/NFC device with embedded fingerprint authentication system“. In 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2017. http://dx.doi.org/10.1109/icsess.2017.8342911.

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Maurizfa und Trio Adiono. „Smart Attendance Recording Device Based on Fingerprint Identification“. In 2021 International Symposium on Electronics and Smart Devices (ISESD). IEEE, 2021. http://dx.doi.org/10.1109/isesd53023.2021.9501823.

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Huang, Qiang, Yubo Song, Junjie Yang, Ming Fan und Aiqun Hu. „A Booting Fingerprint of Device for Network Access Control“. In 2019 3rd International Conference on Circuits, System and Simulation (ICCSS). IEEE, 2019. http://dx.doi.org/10.1109/cirsyssim.2019.8935595.

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Raspopoulos, Marios, Christos Laoudias, Loizos Kanaris, Akis Kokkinis, Christos G. Panayiotou und Stavros Stavrou. „Cross device fingerprint-based positioning using 3D Ray Tracing“. In 2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC 2012). IEEE, 2012. http://dx.doi.org/10.1109/iwcmc.2012.6314193.

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Lin, Yun, Jicheng Jia, Sen Wang, Bin Ge und Shiwen Mao. „Wireless Device Identification Based on Radio Frequency Fingerprint Features“. In ICC 2020 - 2020 IEEE International Conference on Communications (ICC). IEEE, 2020. http://dx.doi.org/10.1109/icc40277.2020.9149226.

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Berichte der Organisationen zum Thema "Device fingerprint"

Stanton, Brian C., Mary Frances Theofanos, Susanne M. Furman, John M. Libert, Shahram Orandi und John D. Grantham. Usability testing of a contactless fingerprint device: part 1. Gaithersburg, MD: National Institute of Standards and Technology, Dezember 2016. http://dx.doi.org/10.6028/nist.ir.8158.

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Stanton, Brian C., Mary Frances Theofanos, Susanne M. Furman und Patrick J. Grother. Usability testing of a contactless fingerprint device: part 2. Gaithersburg, MD: National Institute of Standards and Technology, Dezember 2016. http://dx.doi.org/10.6028/nist.ir.8159.

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Furman, Susanne M., Brian C. Stanton, Mary Frances Theofanos, John M. Libert und John D. Grantham. Contactless fingerprint devices usability test. Gaithersburg, MD: National Institute of Standards and Technology, März 2017. http://dx.doi.org/10.6028/nist.ir.8171.

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Jansen, Wayne, Ronan Daniellou und Nicolas Cilleros. Fingerprint identification and mobile handheld devices :. Gaithersburg, MD: National Institute of Standards and Technology, 2006. http://dx.doi.org/10.6028/nist.ir.7290.

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Libert, John, John Grantham, Bruce Bandini, Stephen Wood, Michael Garris, Kenneth Ko, Fred Byers und Craig Watson. Guidance for evaluating contactless fingerprint acquisition devices. Gaithersburg, MD: National Institute of Standards and Technology, Juli 2018. http://dx.doi.org/10.6028/nist.sp.500-305.

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