Relevant bibliographies by topics / Browser fingerprint

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Browser fingerprint'

Author: Grafiati
Published: 28 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "Browser fingerprint"

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Gabryel, Marcin, Konrad Grzanek, and 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, no. 4 (October 1, 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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Jiang, Wei, Xiaoxi Wang, Xinfang Song, Qixu Liu, and Xiaofeng Liu. "Tracking your browser with high-performance browser fingerprint recognition model." China Communications 17, no. 3 (March 2020): 168–75. http://dx.doi.org/10.23919/jcc.2020.03.014.

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Moskovchenko, Valeriy, Danil Stolyarov, Aleksandr Gorbunov, and Vladislav Belyanin. "The Analysis of Technologies Protecting from Web Browsers Identification." NBI Technologies, no. 1 (August 2018): 34–39. http://dx.doi.org/10.15688/nbit.jvolsu.2018.1.6.

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In the age of information technology, it is becoming increasingly difficult to maintain privacy. Sometimes anonymity on the Internet helps to protect everyone’s right. Anonymity on the Internet also helps to protect against possible illegal actions of third parties. There is a number of technologies that you can use to monitor site user activity. These include technologies such as cookies and fingerprints. Today, cookies technology is an important component of most operations on the Internet. This technology is considered to be one of the main tools that Internet resource owners use to track customers. However, this technique is gradually becoming obsolete and often does not give the desired effect. Fingerprint technology is a global identifier. Browser typos make its owner more recognizable not only on frequently visited Internet resources, but also in other electronic sources. Fingerprints capture the holistic picture that a resource receives from a web browser. This allows you to identify the client even if you make changes to your browser settings. This article deals with the problem of anonymity preservation in a network. The authors describe the main technologies for tracking the users’ website activity, the principles of their work, and the protection methods against these technologies. The advantages and disadvantages of the cookies and fingerprint technologies have also been determined.
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Al-Fannah, Nasser Mohammed, and Chris Mitchell. "Too little too late: can we control browser fingerprinting?" Journal of Intellectual Capital 21, no. 2 (January 7, 2020): 165–80. http://dx.doi.org/10.1108/jic-04-2019-0067.

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Purpose Browser fingerprinting is increasingly being used for online tracking of users, and, unlike the use of cookies, is almost impossible for users to control. This has a major negative impact on online privacy. Despite the availability of a range of fingerprinting countermeasures as well as some limited attempts by browser vendors to curb its effectiveness, it remains largely uncontrolled. The paper aims to discuss this issue. Design/methodology/approach This paper provides the first comprehensive and structured discussion of measures to limit or control browser fingerprinting, covering both user-based and browser-based techniques. Findings This study discusses the limitations of counter browser fingerprinting measures and the need for browser vendor support in controlling fingerprinting. Further, a somewhat counterintuitive possible new browser identifier is proposed which could make cookies and fingerprint-based tracking redundant; the need for, and possible effect of, this feature is discussed. Originality/value This study provides the first comprehensive and structured discussion of measures to limit or control browser fingerprinting. Also, it proposes a new browser identifier that could make cookies and fingerprint-based tracking redundant.
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Iliou, Christos, Theodoros Kostoulas, Theodora Tsikrika, Vasilis Katos, Stefanos Vrochidis, and Ioannis Kompatsiaris. "Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics." Digital Threats: Research and Practice 2, no. 3 (July 2021): 1–26. http://dx.doi.org/10.1145/3447815.

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Web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint, support the main browser functionalities, and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour that reduce their detectability. This work proposes a web bot detection framework that comprises two detection modules: (i) a detection module that utilises web logs, and (ii) a detection module that leverages mouse movements. The framework combines the results of each module in a novel way to capture the different temporal characteristics of the web logs and the mouse movements, as well as the spatial characteristics of the mouse movements. We assess its effectiveness on web bots of two levels of evasiveness: (a) moderate web bots that have a browser fingerprint and (b) advanced web bots that have a browser fingerprint and also exhibit a humanlike behaviour. We show that combining web logs with visitors’ mouse movements is more effective and robust toward detecting advanced web bots that try to evade detection, as opposed to using only one of those approaches.
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Queiroz, Jordan S., and Eduardo L. Feitosa. "A Web Browser Fingerprinting Method Based on the Web Audio API." Computer Journal 62, no. 8 (January 22, 2019): 1106–20. http://dx.doi.org/10.1093/comjnl/bxy146.

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Abstract Web Browser Fingerprinting is a process in which the users are, with high likelihood, uniquely identified by the extracted features from their devices, generating an identifier key (fingerprint). Although it can be used for malicious purposes, especially regarding privacy invasion, Web Browser Fingerprinting can also be used to enhance security (e.g. as a factor in two-factor authentication). This paper investigates the use of Web Audio API as a Web Browser Fingerprinting method capable of identifying the devices. The idea is to prove or not if audio can provide features capable to identify users and devices. Our initial results show that the proposed method is capable of identifying the device’s class, based on features like device’s type, web browser’s version and rendering engine.
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Iskhakov, A. Y., and A. A. Salomatin. "Estimation of the time for calculating the attributes of browser fingerprints in the user authentication task." E3S Web of Conferences 224 (2020): 01030. http://dx.doi.org/10.1051/e3sconf/202022401030.

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This paper presents an overview of the essence of web space which may contain information required to identify and authenticate users on the Internet in order to prevent attempts at malicious acts. Essences with a common content or the same detection method are structured into groups. The greatest attention is paid to one of these groups of entities: «the browser fingerprint» group. An approach and software have been proposed that allow for automated search of the values of entities belonging to this group and for estimating the time of this search for a specific infrastructure in order to develop adaptive authentication mechanisms. Moreover, an analysis of the average time to calculate the values of attributes has been carried out for the most informative browser fingerprints.
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Awale, Mahendra, and Jean-Louis Reymond. "A multi-fingerprint browser for the ZINC database." Nucleic Acids Research 42, W1 (April 29, 2014): W234—W239. http://dx.doi.org/10.1093/nar/gku379.

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García, Óscar Muñoz, Javier Monterrubio Martín, and Daniel García Aubert. "Detecting browser fingerprint evolution for identifying unique users." International Journal of Electronic Business 10, no. 2 (2012): 120. http://dx.doi.org/10.1504/ijeb.2012.051116.

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Avram, Camelia, Jose Machado, and Adina Aştilean. "Hardware Passwords Manager Based on Biometric Authentication." Engineering Proceedings 6, no. 1 (May 17, 2021): 31. http://dx.doi.org/10.3390/i3s2021dresden-10085.

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This paper presents a portable passwords manager which has a two-stage biometric-based access procedure. Data security using biometric methods was chosen as a variant of reduced complexity but was very effective in preventing cyber theft. The implementation of biometrics for the purpose of identification in high-security systems has become essential with the evolution of technology and the spike in identity theft. Unlike passwords or IDs, a biometric feature is an identifier that cannot be lost, stolen, or replicated, which provides biometric authentication systems with an increased level of security. During the first accessing step, the 3DPassManager portable device measures the heartbeat and uses fingerprint and iris features to realize a unique biometric-based authentication. While the specific characteristics of fingerprint and iris features are integrated to ensure that the person using the device is the rightful owner, the pulse is utilized to verify if previously acquired static images are not used. During the second accessing step, a password is generated based on fingerprint details, valid only for a small-time interval. The fingerprint is stored in a secret key with a 1024-bit length. Once access is allowed, the passwords are made available through an extension installed on the web browser. The device is the size of a cigarette pack and communicates with the PC by scanning a QR code. It is safe and was previously tested for dictionary and brute force attacks.
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Dissertations / Theses on the topic "Browser fingerprint"

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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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Švancár, Matúš. "Přenos bezpečnostních opatření z prohlížeče Brave do rozšíření JavaScript Restrictor." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445495.

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Users of internet browsers are constantly monitored, without their consent. By using the JavaScript APIs, it is possible to obtain various information about the browser, which together form a browser fingerprint, which can then be misused. Therefore, the goal of this work is to use a robust fingerprint protection solution of Brave browser and port it to the JavaScript Restrictor extension. In this work, the problematics of obtaining an fingerprint and countermeasures in the Brave browser are analyzed and then compared with the current protection in the JSR extension. The method of porting of Brave's countermeasures is presented and subsequently the procedure of implementation of these defense elements into the browser extension is described. The resulting implementation has been tested and evaluated, with the new protection appearing to be effective.
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Saloň, Marek. "Detekce metod zjišťujících otisk prohlížeče." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445521.

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The main goal of this thesis is to design and implement a mechanism that provides protection against stateless tracking with browser fingerprint. Implemented tool has a form of module that takes part of JavaScript Restrictor extension. The module allows to specify heuristics used for evaluation of visited sites that may contain browser fingerprint extraction. If suspicious activity is detected, all subsequent HTTP requests from that site are blocked to prevent the extracted fingerprint from being sent to the server. The implementation and defined heuristics were tested. The resulting module represents an effective tool against stateless tracking. The main limitation of the implementation is possible corruption of sites by blocking HTTP requests.
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Book chapters on the topic "Browser fingerprint"

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Andriamilanto, Nampoina, Tristan Allard, and Gaëtan Le Guelvouit. "“Guess Who?” Large-Scale Data-Centric Study of the Adequacy of Browser Fingerprints for Web Authentication." In Innovative Mobile and Internet Services in Ubiquitous Computing, 161–72. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50399-4_16.

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Harcourt, Alison, George Christou, and Seamus Simpson. "Engineers and the Public Interest." In Global Standard Setting in Internet Governance, 175–88. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198841524.003.0010.

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Although tracking via social media platforms is widely recognized, as demonstrated by the Cambridge Analytica/Facebook scandal in 2018, other forms of tracking are less well known. Devices such as computers, cameras, and mobile phones can be identified by indicators such as screen size, software versions, and installed fonts. This is called ‘browser fingerprinting’. Commercial software can ‘fingerprint’ web browsers and analyse use without users’ knowledge, even when these users are aware enough to block browsing cookies and/or use virtual private networks (VPNs). This raised significant interest and concern from the engineering community which prompted a number of initiatives within standards-developing organizations (SDOs). This chapter covers case studies detailing work to mitigate the evasion of personal privacy in protocol development within the Internet Engineering Task Force (IETF) and the World Wide Web Consortium (W3C). It demonstrates how SDO work is flanked and supported externally by academics, digital rights groups, and wider civil society organizations.
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Gupta, Brij B., Somya Ranjan Sahoo, Vaibhav Bhatia, Adil Arafat, and Abhik Setia. "Auto Fill Security Solution Using Biometric Authentication for Fake Profile Detection in OSNs." In Handbook of Research on Intrusion Detection Systems, 237–62. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2242-4.ch012.

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This chapter discusses a model that allows the user to access social networking sites through login using smart phone-based biometric authentication. Currently, social networking websites permit the user to access their page through login and some sites provide auto fill system to login into users account through browser by permit. The browser saves the password in password protected space and automatically auto fills the password to access the account by user. This facility is not highly reliable due to the auto fill system for laptop users. When someone uses the laptop of others and visits any website, the auto fill system opens the content with saved password. Secondly, elderly people have problems logging into today's websites. To remember the password for every account is very difficult for elderly people. This chapter describes a model for security and authenticity. Authors used a hybrid model with android as the application with fingerprint authentication and chrome extension as the auto fill process for user access.
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Senthil Kumar A. V. and Rathi M. "Keystroke Dynamics." In Biometric Authentication in Online Learning Environments, 183–207. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7724-9.ch008.

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Online learning has entirely transformed the way of learning by the students. Online tests and quizzes play an important role in online learning, which provides accurate results to the instructor. But, the learners use different methods to cheat during online exams such as opening a browser to search for the answer or a document in the local drive, etc. They are not authenticated once they login and progress to attend the online exams. Different techniques are used in authenticating the students taking up the online exams such as audio or video surveillance systems, fingerprint, or iris recognition, etc. Keystroke dynamics-based authentication (KDA) method, a behavioral biometric-based authentication model has gained focus in authenticating the users. This chapter proposes the usage of KDA as a solution to user authentication in online exams and presents a detailed review on the processes of KDA, the factors that affect the performance of KDA, their applications in different domains, and a few keystroke dynamics-based datasets to authenticate the users during online exams.
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Senthil Kumar A. V. and Rathi M. "Keystroke Dynamics." In Research Anthology on Developing Effective Online Learning Courses, 1137–61. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8047-9.ch056.

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Online learning has entirely transformed the way of learning by the students. Online tests and quizzes play an important role in online learning, which provides accurate results to the instructor. But, the learners use different methods to cheat during online exams such as opening a browser to search for the answer or a document in the local drive, etc. They are not authenticated once they login and progress to attend the online exams. Different techniques are used in authenticating the students taking up the online exams such as audio or video surveillance systems, fingerprint, or iris recognition, etc. Keystroke dynamics-based authentication (KDA) method, a behavioral biometric-based authentication model has gained focus in authenticating the users. This chapter proposes the usage of KDA as a solution to user authentication in online exams and presents a detailed review on the processes of KDA, the factors that affect the performance of KDA, their applications in different domains, and a few keystroke dynamics-based datasets to authenticate the users during online exams.
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Conference papers on the topic "Browser fingerprint"

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Li, Song, and Yinzhi Cao. "Who Touched My Browser Fingerprint?" In IMC '20: ACM Internet Measurement Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3419394.3423614.

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Vastel, Antoine, Pierre Laperdrix, Walter Rudametkin, and Romain Rouvoy. "FP-STALKER: Tracking Browser Fingerprint Evolutions." In 2018 IEEE Symposium on Security and Privacy (SP). IEEE, 2018. http://dx.doi.org/10.1109/sp.2018.00008.

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Antonio, Elbren, Arnel Fajardo, and Ruji Medina. "Tracking Browser Fingerprint using Rule Based Algorithm." In 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA). IEEE, 2020. http://dx.doi.org/10.1109/cspa48992.2020.9068729.

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Vastel, Antoine, Walter Rudametkin, and Romain Rouvoy. "FP -TESTER : Automated Testing of Browser Fingerprint Resilience." In 2018 3rd IEEE European Symposium on Security and Privacy Workshops (EuroS&PW). IEEE, 2018. http://dx.doi.org/10.1109/eurospw.2018.00020.

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Li, Xiaoyun, Xiang Cui, Limin Shi, Chaoge Liu, and Xiaoxi Wang. "Constructing Browser Fingerprint Tracking Chain Based on LSTM Model." In 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). IEEE, 2018. http://dx.doi.org/10.1109/dsc.2018.00038.

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Laperdrix, Pierre, Walter Rudametkin, and Benoit Baudry. "Mitigating Browser Fingerprint Tracking: Multi-level Reconfiguration and Diversification." In 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE, 2015. http://dx.doi.org/10.1109/seams.2015.18.

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Yamada, Tomotaka, Takamichi Saito, Ko Takasu, and Naoki Takei. "Robust Identification of Browser Fingerprint Comparison Using Edit Distance." In 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA). IEEE, 2015. http://dx.doi.org/10.1109/bwcca.2015.106.

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Yokoyama, Shingo, and Ryuya Uda. "A proposal of preventive measure of pursuit using a browser fingerprint." In IMCOM '15: The 9th International Conference on Ubiquitous Information Management and Communication. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2701126.2701210.

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Wu, Tianqi, Yubo Song, Fan Zhang, Shang Gao, and Bin Chen. "My Site Knows Where You Are: A Novel Browser Fingerprint to Track User Position." In ICC 2021 - IEEE International Conference on Communications. IEEE, 2021. http://dx.doi.org/10.1109/icc42927.2021.9500556.

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Laperdrix, Pierre, Walter Rudametkin, and Benoit Baudry. "Beauty and the Beast: Diverting Modern Web Browsers to Build Unique Browser Fingerprints." In 2016 IEEE Symposium on Security and Privacy (SP). IEEE, 2016. http://dx.doi.org/10.1109/sp.2016.57.

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