DOCSLIB.ORG

Device Fingerprinting for Authentication

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Device Fingerprinting for Authentication ELECO 2018, Elektrik-Elektronik ve Biyomedikal Mühendisliği Konferansı 30 Kasım-1 Aralık 2018 Device Fingerprinting for Authentication Zulfidin Khodzhaev Cem Ayyildiz Gunes Karabulut Kurt Istanbul Technical University GOHM North Campus Technopark Istanbul Technical University Istanbul, Turkey Bogazici University Istanbul, Turkey [email protected] [email protected] [email protected] Abstract —Device fingerprinting is a technique that is used to others; accelerometers, gyroscopes and magnetometers which identify and authenticate a device. Different methods are used are called as MEMS components; LCD screen, microphone for this purpose; imperfections of built-in components of the and loudspeaker [2]. device and radio frequency (RF) emissions of the device can be used for authentication. The device can be tested internally There are privacy risks based on these techniques and they or externally; externally is more reliable. Transmission control will be discussed in the next section. Techniques that are protocol is a preferred method of authentication due to its used in device fingerprinting by analyzing these components reliability in precision which is intrinsic for device functionality are discussed in the next section. Mobile phones and other and has unique characteristics for every device. In this paper, devices can be investigated and authenticated using different different techniques on device fingerprinting was analyzed, the techniques based on extraction and analysis of the signal technique based on Transmission control protocol with data transfer rates was tested and the comparison between different obtained from a device either externally or internally. mobile devices was visualized. One of the ways of analyzing signal obtained from a Keywords—Fingerprint, MAC, timestamps, TCP. device is using display of the device,and for this purpose capacitive touchscreen can be used with 99.5 % precision in identification [4]; radio frequency emission of the screen can I. INTRODUCTION be used with the best performance shown by Support Vector Device fingerprinting is a technique used to identify and Machine (SVM) with 98.9 % accuracy on classification of distinguish one brand or model of device from another brand devices [5]. Another way of device fingerprinting is using or model by means of device’s hardware and software config- emissions generated by the components of the device and uration [1]. Usually, tiny differences in the electronic parts of unauthorized devices that try to connect to a network can a device is called fingerprints and those components can be be identified by Radio Frequency-Distinct Native Attribute used for the identification of a mobile phone. It is possible to (RF-DNA) information of malicious device; signal can be analyze those parts if they can generate observable characteris- classified using Fisher based Multiple Discriminant Analysis tics that can be collected and analyzed with a reasonable level and by using Maximum Likelihood with 85 % success rate [6] of precision. Analyzing digital outputs of these differences in and spoofing attacks can be prevented by using Generalized devices such as a mobile phone, gives a way to authenticate Relevance Learning Vector Quantization-Improved (GRLVQI) the device; it can also be used to track the device and its user classifier with 100 % accuracy [6]. which is a privacy risk for an individual using that device [2]. There is also another approach on device fingerprinting Authentication is the process of verifying the identity of a using an external system - medium access control and TCP device and built-in accelerometer or RF emission can be used layer testing. For the former case, difference in timing like for this matter. The most secure methods of authentication clock skews over Wireless Local Area Network (WLAN) is is by investigating fingerprints of the device’s components used to identify and validate a device which can pose a thread which are hard to clone. Device authentication is needed to to user since it doesn’t require any permission. For the latter be secure from counterfeiting and intellectual property rights case, timestamps of Transport Communication Protocol from violation of electronics devices [3]. Fingerprints produced by RFC 1323 is analyzed for fingerprinting and it also creates the components of the device can be obtained internally or privacy risks since there is no cooperation with the device or externally. In the former case, the counterfeited device can any permission from the device. create fabricated fingerprints; the latter case is more effective Another approach on device fingerprinting targets measur- while analyzing compromised device. ing clock drift of a device compared to GNSS and one way is Components that can be used to identify and analyze device to use Georgia Tech ID (GTID) method which processes signal fingerprint are: RF components for sending and receiving coming from a device using statistical techniques by analyzing different cellular communication standards and short range the network in time; another way is by using radio frequency communication such as GSM, UMT, LTE, Bluetooth and Wi- (RF) oscillator of a device, variations in phased locked loops’ Fi; digital cameras that exist in every modern cellular phone; phase noise is used to extract device fingerprint and analyze Global Navigation Satellite Systems (GNSS) receiver which them as a reliable method since the user can not modify it. is used to process signal from GPS, GLONAS, Galileo and The proposed approach is based on performance of the TCP layer of mobile phones against the data transfer. The performance of each device will be uniquely characteristic 978- 605- 01- 1240- 5/sk 2018 722 193 ELECO 2018, Elektrik-Elektronik ve Biyomedikal Mühendisliği Konferansı 30 Kasım-1 Aralık 2018 and the difference will be graphed for visual representation. Global System for Mobile Communication, Universal Mobile In our method, the Transmission Control Protocol (TCP) with Telecommunication System. Each component or communica- data transfer rates were tested and the obtained data was fed tion standard has its own imperfections and tiny differences into a k-nearest neighbors algorithm. The difference between which can be intrinsic or can be observed while performing different devices was visualized. any task; these small variations present a distinction between components or communication standards available in a device. II. DEVICE FINGERPRINTING TECHNIQUES Component imperfections can be used to classify or distinguish between devices. Gaussian Minimum Shift Keying is used Device can be analyzed by using digital output of compo- in Global System for Mobile Communication networks and nents of the device. Signal obtained from an analysis of device signals coming from Gaussian Minimum Shift Keying can components can be processed by an external or an internal be used as a Radio Frequency fingerprint of a device; these system. External system uses RF emissions that comes from emitted signals can be classified to identify a device which in components of a device, to analyze and process the signal. On turn can provide physical layer security [7]. the other hand, internal system will be connected to the device and data will be extracted from a device and then it will be Also, there is a security concern on wireless application processed. Privacy risks that arise from outside analysis of a protocols access points; unauthorized devices can try to con- device are mainly based on RF emissions; other components nect to the network or a device can try to masquerade as of the device i.e. screen, loudspeaker are difficult to track. another device by falsifying data. Devices that try to connect to a network without authorization can be identified by Radio Frequency-Distinct Native Attribute (RF-DNA) information A. Device fingerprinting by using an external system coming from unauthorized machine. Radio Frequency-Distinct One of the techniques used in device fingerprinting is Native Attribute fingerprint is specific to a device due to executed by an external system using display of the device. implementation of the hardware or component type; these Display of a device can be used to authenticate the device by fingerprints can be classified using Fisher based Multiple measuring amount of the pressure that a person delivers to Discriminant Analysis and by using Maximum Likelihood. the capacitive touchscreen panel thus identification based on Classification of the fingerprint of a device gives an approach features of the body will be achieved; device fingerprinting to compare the received Radio Frequency-Distinct Native At- technique based on capacitive touchscreen panel using an tribute with the stored reference information; this comparison external system in display of the device is a method that gives out the device that is included in to the dataset which in identifies users with 99.5 % of precision [4]. turn provides authentication of a device. By using Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) Fingerprinting technique using display of the device also classifier, spoofing attacks were detected with 100 % success makes possible for a device to be identified by their screens’ rate and when Fisher based Multiple Discriminant Analysis Radio Frequency emissions; there is a possibility of using and Maximum Likelihood was used to detect unauthorized Artificial Neural Network and SVM for identification of radio devices, the success rate of detection was 85 % [6]. frequency emission of the devices’ screen since
Load more

Recommended publications
  • Vmlogin Virtual Multi-Login Browser Instruction Ver
    VMLogin virtual multi-login browser Instruction Ver 1.0 Skype: [email protected] Telegram1: Vmlogin Telegram2: vmlogin_us System Requirements Hardware requirements RAM: 4GB recommended; 1GB available disk space Effective GPU Supported operating systems Although VMLogin supports x86 (32-bit) systems, we recommend using VMLogin on x64 (64-bit) systems. OS supported by VMLogin Windows 10 Windows Server 2016 Windows 7 Windows 8 Software install Users can download the latest software installation package from the official website: vmlogin.us. The version used in this manual is 1.0.3.8. We can start the software installation package program, and the interface for selecting the installation language will be displayed: User can select the software language he likes, and can also change the language in the settings after installation. Currently supports Simplified Chinese and English. After the user select the language, enter the interface for selecting the installation directory: Here is generally installed by default, press the "Next" button, if the user customizes the installation directory, please pay attention to the directory permissions, the current user can edit. Generally, you need to create a desktop shortcut for convenient, we click the "Next" button to continue by default here. At this point we can click the "Install" button to install. We need to wait for the installation progress to complete. At this step, the software installation is complete. Software function 1. Start Software When we start the software, we will see the login interface. New users need to create a new account to log in. 2. Create Account To register as a new user, you need to use email as your account, set a password, and register.
    [Show full text]
  • Device Fingerprinting for Augmenting Web Authentication: Classification and Analysis of Methods
    Device Fingerprinting for Augmenting Web Authentication: Classification and Analysis of Methods Furkan Alaca P.C. van Oorschot School of Computer Science Carleton University, Ottawa, Canada ABSTRACT ing habits [21,2]. In addition, a number of commercial Device fingerprinting is commonly used for tracking users. fraud detection services [38, 57] employ browser-based de- We explore device fingerprinting but in the specific context vice fingerprinting to identify fraudulent transactions. Large of use for augmenting authentication, providing a state-of- web services can mitigate insecure passwords in part by the-art view and analysis. We summarize and classify 29 using server-side intelligence in the authentication process available methods and their properties; define attack models with \multidimensional" authentication [14], wherein con- relevant to augmenting passwords for user authentication; ventional passwords are augmented by multiple implicit sig- and qualitatively compare them based on stability, repeata- nals collected from the user's device without requiring user bility, resource use, client passiveness, difficulty of spoofing, intervention. We explore the applicability of various finger- and distinguishability offered. printing techniques to strengthening web authentication and evaluate them based on the security benefits they provide. CCS Concepts We aim to identify techniques that can improve security, while being ideally invisible to users and compatible with •Security and privacy ! Authentication; Web appli- existing web browsers, thereby imposing zero cost on us- cation security; ability and deployability. We identify and classify 29 available device fingerprint- Keywords ing mechanisms, primarily browser-based and known, but Device fingerprinting, multi-dimensional authentication, pass- including several network-based methods and others not in words, comparative analysis, comparative criteria the literature; identify and assess their properties suitable for application to augment user authentication; define a se- 1.
    [Show full text]
  • Threatmetrix Whitepaper
    Smart Device Identification for Cloud-Based Fraud Prevention Alisdair Faulkner Chief Products Officer White Paper: Smart Device Identification for Cloud-Based Fraud Prevention Contents Basic Device Identification is no longer enough ........ 3 Times have changed but your Device ID hasn’t .......................................................................... 3 Cookies are Obsolete .................................................................................................................. 5 Device Fingerprints Smudge and Fraudsters Wear Gloves ........................................................ 6 Compromised Devices are Commodities .................................................................................... 7 Smart Device Identification Requirements .................................................................................. 8 Smart versus Basic Device Identification Comparison ................................................................ 9 ThreatMetrix Smart Device Identification ................. 11 Identify Fraudsters and Authenticate Customers ...................................................................... 11 Cookieless Device Fingerprinting .............................................................................................. 12 IP, Browser and Packet Fingerprint Interrogation ..................................................................... 13 Real-time complex attribute matching and confidence scoring ................................................. 15 Man-In-The-Middle/Hidden Proxy
    [Show full text]
  • Web-Based Fingerprinting Techniques
    Web-based Fingerprinting Techniques V´ıtor Bernardo and Dulce Domingos LaSIGE, Faculdade de Ciencias,ˆ Universidade de Lisboa, Lisboa, Portugal Keywords: Browser Fingerprinting, Cross-browser Fingerprinting, Device Fingerprinting, Privacy, Fingerprint. Abstract: The concept of device fingerprinting is based in the assumption that each electronic device holds a unique set of physical and/or logical features that others can capture and use to differentiate it from the whole. Web-based fingerprinting, a particular case of device fingerprinting, allows website owners to differentiate devices based on the set of information that browsers transmit. Depending on the techniques being used, a website can track a device based on its browser features (browser fingerprinting) or based on system settings (cross-browser fingerprinting). The latter allows identification of the device even when more than one browser is used. Several different works have introduced new techniques over the last years proving that fingerprinting can be done in multiple ways, but there is not a consolidated work gathering all of them. The current work identifies known web-based fingerprinting techniques, categorizing them as which ones are browser and which are cross-browser and showing real examples of the data that can be captured with each technique. The study is synthesized in a taxonomy, which provides a clear separation between techniques, making it easier to identify the threats to security and privacy inherent to each one. 1 INTRODUCTION far more upsetting than simple cookies. In most cases, web-based fingerprinting is used to Device fingerprinting is based on the assumption that track users activity in sites and bind a device finger- no two devices are exactly alike and that profiles can print to a user profile (together with its preferences, be created by capturing the emanation patterns sent tastes and interests).
    [Show full text]
  • The Elephant in the Background: a Quantitative Approach to Empower Users Against Web Browser Fingerprinting
    The Elephant in the Background: A Quantitative Approach to Empower Users Against Web Browser Fingerprinting Anonymous Abstract—Tracking users is a ubiquitous practice in the web Furthermore, other researchers have concentrated their ef- today. User activity is recorded on a large scale and analyzed forts on creating new fingerprinting techniques, e.g. by using by various actors to create personalized products, forecast future canvas [24] and audio objects [30] or via CSS [37]. Others behavior, and prevent online fraud. While so far HTTP cookies have significantly improved known techniques with machine have been the weapon of choice, new and more pervasive learning [42] or by extracting fingerprinting features more techniques such as browser fingerprinting are gaining traction. passively via extension activity tracking [36]. Hence, in this paper, we describe how users can be empowered against JavaScript fingerprinting by showing them when, how, The sheer diversity of fingerprinting techniques available and who is tracking them. To this end, we conduct a systematic analysis of various JavaScript fingerprinting tools. Based on this to us begs the question of which techniques are really being analysis, we design and develop FPMON: a light-weight and used and which ones are not? So far, large scale studies have comprehensive fingerprinting monitor that measures and rates only uncovered specific instances of fingerprinting and these JavaScript fingerprinting activity on any given website in real- are over 5-10 years old [1,2]. In particular, we currently lack time. Using FPMON, we evaluate the Alexa 10k most popular a comprehensive view of fingerprinting activity on the web websites to i) study the pervasiveness of JavaScript fingerprinting; today.
    [Show full text]
  • Browser Extension and Login-Leak Experiment
    BROWSER EXTENSION AND LOGIN-LEAK EXPERIMENT IPEN 2017, Vienna Joint work with Nataliia Bielova, Claude Castelluccia Gábor György Gulyás Privatics Team, INRIA http://gulyas.info // @GulyasGG USER TRACKING ON THE WEB 17-06-09 © Gábor György Gulyás 2 The „de-facto” busniess model of the web Advertiser ID=967 User Apples on sale! ID=967 cnn.com 17-06-09 © Gábor György Gulyás 3 Storing the identifier on the client side • Cookies • E-tags – Flash • Last-mod timestamps – HTML5 • HTTP authentication • Caching in files of • HTTP 301 redirect – JavaScript • HSTS caches – CSS – Images (pixel-level) … 17-06-09 © Gábor György Gulyás 4 Browser fingerprinting appears (2010-2012) [3] http://panopticlick.eff.org https://fingerprint.pet-portal.eu • Browser fingerprint • Cross-browser fingp. – Flash/Java required – Device fingerprint (for 95% uniqueness) – No plugins, just JS – Browser dependent – Concept appears later in the wild 17-06-09 © Gábor György Gulyás 5 Fingerprinting penetration (2013-2016) 2013: Alexa TOP 10k. • 20 pages deep • 0,4% adoption (40 sites) • Skype.com, porn and dating • 3 804 less popular sites are tracked Nickiforakis et al.: Cookieless monster: Exploring the ecosystem of web-based device fingerprinting (2013) 2016: Alexa TOP 1M. S. Englehardt, A. Narayanan: Online tracking: A 1-illion-site measurement and analysis (2016) 17-06-09 © Gábor György Gulyás 6 Behavioral fingerprinting You are what you install to you computer? Fonts are good indicators of what is installed. Boda et al.: User Tracking on the Web via Cross-Browser Fingerprinting (2011) Google.com Youtube.com Facebook.com Baidu.com The list of the sites you have Yahoo.com Wikipedia.org visited also describe you well.
    [Show full text]
  • The Elephant in the Background: a Quantitative Approach to Empower Users Against Web Browser Fingerprinting
    EasyChair Preprint № 4473 The Elephant in the Background: A Quantitative Approach to Empower Users Against Web Browser Fingerprinting Julian Fietkau, Kashjap Thimmaraju, Felix Kybranz, Sebastian Neef and Jean-Pierre Seifert EasyChair preprints are intended for rapid dissemination of research results and are integrated with the rest of EasyChair. October 26, 2020 The Elephant in the Background: A Quantitative Approach to Empower Users Against Web Browser Fingerprinting Julian Fietkau, Kashyap Thimmaraju∗, Felix Kybranz, Sebastian Neef, Jean-Pierre Seifert Technische Universitat¨ Berlin, Humboldt-Universitat¨ Berlin∗ ffietkau, f.kybranz, [email protected], fkash, [email protected] Abstract—Tracking users is a ubiquitous practice in the web Furthermore, other researchers have concentrated their ef- today. User activity is recorded on a large scale and analyzed forts on creating new fingerprinting techniques, e.g. by using by various actors to create personalized products, forecast future canvas [24] and audio objects [30] or via CSS [37]. Others behavior, and prevent online fraud. While so far HTTP cookies have significantly improved known techniques with machine have been the weapon of choice, new and more pervasive learning [42] or by extracting fingerprinting features more techniques such as browser fingerprinting are gaining traction. passively via extension activity tracking [36]. Hence, in this paper, we describe how users can be empowered against JavaScript fingerprinting by showing them when, how, The sheer diversity of fingerprinting
    [Show full text]
  • Future of Device Fingerprinting
    WHITE PAPER Future of Device Fingerprinting Contents Introduction 1 Limitations of Traditional Device Fingerprinting 2 Device Fingerprinting Evolution 3 How Simility’s Device Recon Works 4 Individual Use Cases 7 Today’s device fingerprinting technology has been made significantly more effective at fighting online fraud with advanced techniques such as fuzzy matching, clustering and predictive modeling. Introduction to Device Fingerprinting For over a decade, a crucial part of fraud detection in the virtual world has been assigning an identity to every laptop, tablet and mobile device that accesses a website or app. Such a fingerprint, often referred as a device fingerprint or device ID, is a representation of hundreds of different device-specific values taken from an end user’s device. Like in the real world, a device fingerprint aids in identification and tracking of bad actors. As a first step to any fraud attempt, fraudsters try to hide their identity or pretend to be many different people when they are, in fact, just one person. For example, a person who owns a local barber shop may want to publish a lot of fake positive reviews about his business and negative reviews about the competitors’ businesses on a review app. Obviously, the person will use a fake name because his customers and competitors know about his business. Also, the business review site probably requires a different email address for each account, so he might enter fake email addresses when setting up multiple fake accounts. Perhaps the review app even requires each account to be from a different IP address and to register a new phone number.
    [Show full text]
  • Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic
    JAISCR, 2020, Vol. 10, No. 4, pp. 243 – 253 10.2478/jaiscr-2020-0016 BROWSER FINGERPRINT CODING METHODS INCREASING THE EFFECTIVENESS OF USER IDENTIFICATION IN THE WEB TRAFFIC 1, 2 3 Marcin Gabryel ∗, Konrad Grzanek , Yoichi Hayashi 1Department of Computer Engineering, Czestochowa University of Technology, al. Armii Krajowej 36, 42-200 Cze¸stochowa, Poland 2Information Technology Institute, University of Social Sciences, 90 - 113 Lodz Clark University, Worcester, MA 01610, USA 3Department of Computer Science, Meiji University, Japan ∗E-mail: [email protected] Submitted: 14th October 2019; Accepted: 29th April 2020 Abstract Web-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 extract- ing 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 func- tion. 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, mak- ing 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 pa- rameters.
    [Show full text]
  • A Systematic Framework for Categorising Iot Device Fingerprinting Mechanisms
    This is a repository copy of Position paper : A systematic framework for categorising IoT device fingerprinting mechanisms. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/169799/ Version: Published Version Proceedings Paper: Yadav, Poonam, Feraudo, Angelo, Arief, Budi et al. (2 more authors) (2020) Position paper : A systematic framework for categorising IoT device fingerprinting mechanisms. In: AIChallengeIoT '20: Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things. Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things, 16 Nov 2020 ACM AIChallengeIoT (Sensys 2020) . Association for Computing Machinery (ACM) , 62–68. https://doi.org/10.1145/3417313.3429384 Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Position paper: A systematic framework for categorising IoT device fingerprinting mechanisms Poonam Yadav Angelo Feraudo Budi Arief University of York, UK University of York, UK University of Kent, UK Siamak F.
    [Show full text]
  • Mobile Device Fingerprinting and Unique User Identification Media and Entertainment (M&E)
    White Paper Mobile Device Fingerprinting and Unique User Identification Media and Entertainment (M&E) Table of Contents Executive Summary 01 Introduction 02 Solution Overview 02 Scenario 1: User identification and tracking for mobile web (cookie-less approach) 03 Scenario 2: User identification and tracking with SDK 05 Conclusion 07 Executive Summary The principles of user or device tracking in the online world and the mobile world are basically similar. Identifying and tracking user profiles without Personally Identifiable Information (PII) is something being undertaken by a number of the entities in the delivery of targeted mobile ad campaigns. A device fingerprint is a set of information collected about a device or user profile for the purpose of audience-based content or ad targeting. Although the cookies are turned off, it is still feasible to use fingerprints for complete or partial identification of individual users or devices. Device fingerprinting, sometimes known as digital fingerprinting, claims that it can assist businesses to enhance online or mobile advertising and diminish fraud risk. 01 Introduction The purpose of this whitepaper is to outline various scenarios and implementation approaches for mobile device fingerprinting, unique user identification, and user tracking to: • Get insights into mobile campaign performance events such as clicks and installs • Track which app was installed and which user installed the app • Track users’ behavior and browsing patterns to enhance your content or ad recommendation engine Solution Overview The challenge The key challenge is to uniquely identify a mobile user. Mobile users access content through both native applications and mobile web. On mobile devices, websites and applications are regarded as disparate domains.
    [Show full text]
  • Iot Device Fingerprint Using Deep Learning
    Accepted for publication in 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS) Please refer DOI: 10.1109/IOTAIS.2018.8600824 for final manuscript IoT Device Fingerprint using Deep Learning Sandhya Aneja Nagender Aneja Md Shohidul Islam Faculty of Integrated Technologies Institute of Applied Data Analytics Faculty of Integrated Technologies Universiti Brunei Darussalam Universiti Brunei Darussalam Universiti Brunei Darussalam Brunei Darussalam Brunei Darussalam Brunei Darussalam [email protected] [email protected] [email protected] Abstract—Device Fingerprinting (DFP) is the identification extract IAT that is the time interval between consecutively of a device without using its network or other assigned received packets. In this paper, we worked on the DFP identities including IP address, Medium Access Control using IAT to create IAT fingerprint using deep learning. (MAC) address, or International Mobile Equipment Identity (IMEI) number. DFP identifies a device using information IAT is unique for each device due to the different hardware from the packets which the device uses to communicate over and software used for the device and thus is more useful in the network. Packets are received at a router and processed the wireless networks and Internet of Things (IoT) devices to extract the information. In this paper, we worked on the where communication is highly vulnerable due to identity DFP using Inter Arrival Time (IAT). IAT is the time interval issues. between the two consecutive packets received. This has been observed that the IAT is unique for a device because of This work presents a novel idea of DFP by plotting the different hardware and the software used for the device.
    [Show full text]