biblio.bib

% Potentielement interessant

% Biometrics advantage and inconvenience % biométrie faillible
@article{10.1109/MC.2012.364,
author = {Karthik Nandakumar and Anil K. Jain, },
title = {Biometric Authentication: System Security and User Privacy},
journal = {Computer},
volume = {45},
number = {undefined},
issn = {0018-9162},
year = {2012},
pages = {87-92},
doi = {doi.ieeecomputersociety.org/10.1109/MC.2012.364},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}

% smartphone + biometrics
@INPROCEEDINGS{7568944,
author={D. M. Shila and K. Srivastava and P. O'Neill and K. Reddy and V. Sritapan},
booktitle={2016 IEEE Symposium on Technologies for Homeland Security (HST)},
title={A multi-faceted approach to user authentication for mobile devices; Using human movement, usage, and location patterns},
year={2016},
pages={1-6},
abstract={Mobile devices have now become indispensable and ubiquitous enablers for collaboration. Such a ubiquity increases concerns over the resilience of existing mobile authentication methods and their ability to safeguard the growing amount of sensitive information stored and processed on these devices. Conventional knowledge-based authentication techniques such as PINs, passwords and pattern locks are inadequate to fully realize the current needs of the users due to low user-friendliness and insufficient security. One approach to enhance user-friendliness is to enable authentication merely for sensitive and critical applications, thereby not requiring users to inconveniently authenticate on the device every time it is accessed for e.g., while accessing low security applications such as games, news, and entertainment. However, knowledge-based techniques fail to deliver strong security guarantees for sensitive applications as they are vulnerable to classic cyber-attacks such as brute-force, social engineering, shoulder surfing, and smudge etc. Physiological biometrics such as fingerprint, voice or facial recognition can enable user-friendly and strong security, but they only provide single-shot authentication and lack ability to continuously authenticate the user. In this effort, we take a different approach by designing a multi-faceted authentication scheme termed as mAuth that continuously and unobtrusively authenticates the user while the device is being in contact with the user. By leveraging a combination of supervised and unsupervised learning techniques on the raw low-level sensor data from the mobile device, multiple inferences about the user (or higher-level contexts) such as the frequently visited locations, physical proximity with the device (carrying in the pocket or placed on the table), and gait patterns are extracted. These multiple high-level contexts regarding the user are further fused to generate a dynamic trust score that determines the degree to which- the user is trustworthy to access the applications. Experiments demonstrate the performance of the individual learning algorithms as well as the overall method in identifying users under natural settings. Various attack scenarios targeting mobile devices are designed to prove the security of the proposed approach. We also explore ways to unobtrusively acquire data for supervised learning algorithms without explicit user annotation.},
keywords={gait analysis;inference mechanisms;mobile radio;telecommunication security;trusted computing;ubiquitous computing;unsupervised learning;cyber-attacks;dynamic trust score;gait patterns;knowledge-based authentication techniques;low security applications;low-level sensor data;mobile authentication methods;mobile devices;multi-faceted authentication scheme;physical proximity;physiological biometrics;strong security guarantees;unsupervised learning techniques;user-friendliness;Authentication;Biometrics (access control);Computational modeling;Global Positioning System;Knowledge based systems;Mobile handsets},
doi={10.1109/THS.2016.7568944},
month={May},}

% Smartphone + biometrics
@ARTICLE{7423666,
author={A. Alzubaidi and J. Kalita},
journal={IEEE Communications Surveys Tutorials},
title={Authentication of Smartphone Users Using Behavioral Biometrics},
year={2016},
volume={18},
number={3},
pages={1998-2026},
abstract={Smartphones and tablets have become ubiquitous in our daily lives. Smartphones, in particular, have become more than personal assistants. These devices have provided new avenues for consumers to play, work, and socialize whenever and wherever they want. Smartphones are small in size, so they are easy to handle and to stow and carry in users' pockets or purses. However, mobile devices are also susceptible to various problems. One of the greatest concerns is the possibility of breach in security and privacy if the device is seized by an outside party. It is possible that threats can come from friends as well as strangers. Due to the size of smart devices, they can be easily lost and may expose details of users' private lives. In addition, this might enable pervasive observation or imitation of one's movements and activities, such as sending messages to contacts, accessing private communication, shopping with a credit card, and relaying information about where one has been. This paper highlights the potential risks that occur when smartphones are stolen or seized, discusses the concept of continuous authentication, and analyzes current approaches and mechanisms of behavioral biometrics with respect to methodology, associated datasets and evaluation approaches.},
keywords={behavioural sciences computing;biometrics (access control);cryptography;mobile computing;notebook computers;smart phones;behavioral biometrics;breach in privacy;breach in security;smart device;smartphone user authentication;tablets;Authentication;Biomedical monitoring;Biometrics (access control);Mobile handsets;Privacy;Tutorials;Authentication;Biometrics;Continuous Authentication;Implicit Authentication;Progressive Authentication;Smartphone;User Behavior},
doi={10.1109/COMST.2016.2537748},
ISSN={1553-877X},
month={thirdquarter},}

% sound of keyboard, reliable on a same word
@INPROCEEDINGS{7477360,
author={M. Pleva and E. Kiktova and P. Viszlay and P. Bours},
booktitle={2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA)},
title={Acoustical keystroke analysis for user identification and authentication},
year={2016},
pages={386-389},
abstract={This article describes a new algorithm of calibrated user authentication using acoustical monitoring of keyboard when typing the pre-defined word. The HMM (Hidden Markov Models) with MFCC (Mel-frequency cepstral coefficients) features were used in the setup. In authentication task a low EER (Equal Error Rate) was achieved between 9.4\% and 14.8\% using a calibration setup and 3 sessions training. For identification part the accuracy of 99.33\% was achieved, when testing 25\% of realizations (randomly selected from 100 recordings) identifying between 50 users/models. Calibration was done using one user recording to calibrate the microphone and keyboard table setup when enrolling his model. Genuine and impostor tests were realized for 50 volunteers typing 100 words each in 4 sessions.},
keywords={calibration;cryptography;hidden Markov models;microphones;HMM;Mel-frequency cepstral coefficients features;acoustical keystroke analysis;acoustical monitoring;authentication task;calibrated user authentication;calibration setup;equal error rate;genuine tests;hidden Markov models;impostor tests;keyboard table setup;microphone;pre-defined word;user identification;Authentication;Calibration;Hidden Markov models;Keyboards;Testing;Timing;Training;HMM;MFCC;acoustical analysis;keystroke;user authentication;user identification},
doi={10.1109/RADIOELEK.2016.7477360},
month={April},}

@article{fridman2015multi,
  title={Multi-modal decision fusion for continuous authentication},
  author={Fridman, Lex and Stolerman, Ariel and Acharya, Sayandeep and Brennan, Patrick and Juola, Patrick and Greenstadt, Rachel and Kam, Moshe},
  journal={Computers \& Electrical Engineering},
  volume={41},
  pages={142--156},
  year={2015},
  publisher={Elsevier}
}

% mouse + keyboard
@INPROCEEDINGS{7477228,
author={S. Mondal and P. Bours},
booktitle={2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)},
title={Combining keystroke and mouse dynamics for continuous user authentication and identification},
year={2016},
pages={1-8},
abstract={In this paper, we analyze the performance of a continuous user authentication and identification system for a PC under various analysis techniques. We applied a novel identification technique called Pairwise User Coupling (PUC) on our own dataset for the analysis. This dataset is a combination of keystroke and mouse usage behaviour data. We obtained an identification accuracy of 62.2\% for a closed-set experiment, where the system needs on average of 471 actions to detect an impostor. In case of an open-set experiment the Detection and Identification Rate (DIR) of 58.9\% was obtained, where the system needs on average of 333 actions to detect an impostor.},
keywords={authorisation;continuous user authentication;continuous user identification;impostor detection;keystroke dynamics;mouse dynamics;pairwise user coupling;username-password based access control;Authentication;Couplings;Feature extraction;Mice;Support vector machines;Training;Training data},
doi={10.1109/ISBA.2016.7477228},
month={Feb},}



@INPROCEEDINGS{7498306,
author={W. Cao and Z. Wu and D. Wang and J. Li and H. Wu},
booktitle={2016 IEEE 32nd International Conference on Data Engineering (ICDE)},
title={Automatic user identification method across heterogeneous mobility data sources},
year={2016},
pages={978-989},
abstract={With the ubiquity of location based services and applications, large volume of mobility data has been generated routinely, usually from heterogeneous data sources, such as different GPS-embedded devices, mobile apps or location based service providers. In this paper, we investigate efficient ways of identifying users across such heterogeneous data sources. We present a MapReduce-based framework called Automatic User Identification (AUI) which is easy to deploy and can scale to very large data set. Our framework is based on a novel similarity measure called the signal based similarity (SIG) which measures the similarity of users' trajectories gathered from different data sources, typically with very different sampling rates and noise patterns. We conduct extensive experimental evaluations, which show that our framework outperforms the existing methods significantly. Our study on one hand provides an effective approach for the mobility data integration problem on large scale data sets, i.e., combining the mobility data sets from different sources in order to enhance the data quality. On the other hand, our study provides an in-depth investigation for the widely studied human mobility uniqueness problem under heterogeneous data sources.},
keywords={data integration;information retrieval;mobile computing;AUI;MapReduce-based framework;SIG measure;automatic user identification method;data quality;heterogeneous mobility data sources;location based services;mobility data integration problem;mobility data volume;signal based similarity measure;Buildings;Education;Mobile communication;Navigation;Noise measurement;Trajectory;Urban areas},
doi={10.1109/ICDE.2016.7498306},
month={May},}

% Keyboard
@INPROCEEDINGS{7045794,
author={T. Highlander and D. Bassett and D. Boone},
booktitle={NAECON 2014 - IEEE National Aerospace and Electronics Conference},
title={Utilization of keyboard dynamics for unique identification of human users},
year={2014},
pages={153-156},
abstract={For the past two decades, as the role of computer technology has expanded, computer security (secure processors, information encryption, network protection with biometrics) has also become increasingly important [1-21]. Many computer security schemes have been developed over this time period; however, none of these are foolproof yet. A username and pass-word combination is one of the most popular approaches to securing a computer system, but this system is vulnerable because passwords can be stolen or cracked. However, research suggests that it is possible to identify a user based strictly on their typing style, using pattern recognition, neural networks, and other techniques. This paper focuses on using the keyboard dynamics of the user's password to add an extra layer of security to the natural log-in process.},
keywords={authorisation;biometrics (access control);pattern recognition;computer security;human user identification;keyboard dynamics utilization;natural log-in process;user password;Authentication;Biometrics (access control);Computers;Keyboards;Neural networks;Training},
doi={10.1109/NAECON.2014.7045794},
ISSN={0547-3578},
month={June},}


% Gui manipulation, may usable, unknow reliability
@INPROCEEDINGS{4925100,
author={E. S. Imsand and D. Garrett and J. A. Hamilton},
booktitle={Computational Intelligence in Cyber Security, 2009. CICS '09. IEEE Symposium on},
title={User identification using GUI manipulation patterns and artificial neural networks},
year={2009},
pages={130-135},
abstract={A masquerade attack is any attack in which the attacker is able to make the target system believe they are someone they are not. One particularly dangerous example of a masquerade attack occurs when an attacker uses an unattended, unlocked computer workstation. Recent studies have demonstrated that analyzing the manners in which a user manipulates a graphical user interface can be used as an effective means of authentication. We present the results of a study into the use of GUI manipulations in combination with Artificial Neural Networks (ANNs) as a basis for identification.},
keywords={graphical user interfaces;message authentication;neural nets;GUI manipulation pattern;GUI-based intrusion detection;artificial neural network;authentication;graphical user interface;user identification;Artificial neural networks;Authentication;Computer science;Electronic mail;Graphical user interfaces;Intrusion detection;Monitoring;Operating systems;Software engineering;Workstations},
doi={10.1109/CICYBS.2009.4925100},
month={March},}


% smartphone + biometrics
@INPROCEEDINGS{7017067,
author={C. Bo and L. Zhang and T. Jung and J. Han and X. Y. Li and Y. Wang},
booktitle={2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC)},
title={Continuous user identification via touch and movement behavioral biometrics},
year={2014},
pages={1-8},
abstract={With the increased popularity of smartphones, various security threats and privacy leakages targeting them are discovered and investigated. In this work, we present SilentSense, a framework to authenticate users silently and transparently by exploiting dynamics mined from the user touch behavior biometrics and the micro-movement of the device caused by user's screen-touch actions. We build a “touch-based biometrics” model of the owner by extracting some principle features, and then verify whether the current user is the owner or guest/attacker. When using the smartphone, some unique operating dynamics of the user is detected and learnt by collecting the sensor data and touch events silently. When users are mobile, the micro-movement of mobile devices caused by touch is suppressed by that due to the large scale user-movement which will render the touch-based biometrics ineffective. To address this, we integrate a movement-based biometrics for each user with previous touch-based biometrics. We conduct extensive evaluations of our approaches on the Android smartphone, we show that the user identification accuracy is over 99\%.},
keywords={biometrics (access control);data privacy;message authentication;smart phones;Android smartphone;SilentSense;continuous user identification;large scale user-movement;movement behavioral biometrics;privacy leakage;screen-touch action;security threat;smartphones;touch behavioral biometrics;user authentication;Acceleration;Accuracy;Biometrics (access control);Delays;Feature extraction;Legged locomotion;Privacy},
doi={10.1109/PCCC.2014.7017067},
ISSN={1097-2641},
month={Dec},}


% Face, fingerprint, quite reliable
@INPROCEEDINGS{1612826,
author={T. Ko},
booktitle={34th Applied Imagery and Pattern Recognition Workshop (AIPR'05)},
title={Multimodal biometric identification for large user population using fingerprint, face and iris recognition},
year={2005},
pages={6 pp.-223},
keywords={face recognition;fingerprint identification;visual databases;face recognition;fingerprint recognition;identification accuracy;iris recognition;multimodal biometric identification;secondary human validation;user population identification system;Biometrics;Error analysis;Face;Fingerprint recognition;Fuses;Humans;Image databases;Image quality;Iris recognition;System identification},
doi={10.1109/AIPR.2005.35},
ISSN={1550-5219},
month={Dec},}

% biometrics, shape of lips
@INPROCEEDINGS{5483848,
author={J. C. Briceño and C. M. Travieso and J. B. Alonso and M. A. Ferrer},
booktitle={2010 IEEE 14th International Conference on Intelligent Engineering Systems},
title={Robust identification of persons by lips contour using shape transformation},
year={2010},
pages={203-207},
abstract={In this paper we present a biometric approach, based on lip shape. We have performed an image preprocessing, in order to detect the face of a person image. After this, we have enhanced the lips image using a color transformation, and next we do its detection. The parameterization is based on lips contour points. Those points have been transformed by a Hidden Markov Model (HMM) kernel, using a minimization of Fisher Score. Finally, a one-versus-all multiclass supervised approach based on Support Vector Machines (SVM) is applied as a classifier. A database with 50 users and 10 samples per class has been built. A cross-validation strategy have been applied in our experiments, reaching success rates up to 99.6\%, using four lip training samples per class, and evaluating with six lip test samples. This success was found using a shape of 150 points, with 40 states in Hidden Markov Model and a RBF kernel for a supervised approach based on Support Vector Machines.},
keywords={face recognition;hidden Markov models;image colour analysis;image enhancement;minimisation;radial basis function networks;shape recognition;support vector machines;Fisher score;HMM kernel;RBF kernel;SVM;biometric approach;color transformation;cross-validation strategy;face detection;hidden Markov model;image preprocessing;lip shape;lips contour points;lips image enhancement;minimization;robust person identification;shape transformation;support vector machines;Biometrics;Face detection;Hidden Markov models;Identification of persons;Kernel;Lips;Robustness;Shape;Support vector machine classification;Support vector machines},
doi={10.1109/INES.2010.5483848},
ISSN={1543-9259},
month={May},}


% keyboard
@ARTICLE{1341408,
author={A. Peacock and Xian Ke and M. Wilkerson},
journal={IEEE Security Privacy},
title={Typing patterns: a key to user identification},
year={2004},
volume={2},
number={5},
pages={40-47},
keywords={authorisation;biometrics (access control);keystroke biometrics;typing patterns;user identification;Authentication;Biometrics;Computer security;Costs;Marketing and sales;Privacy;Rhythm;Telegraphy;Timing;Usability;Authentication;Biometrics;Intellectual Property;Keystroke;Pattern Recognition;Security;Security and Privacy Protection;Typing;User Authentication;User Identification;network-level security and protection},
doi={10.1109/MSP.2004.89},
ISSN={1540-7993},
month={Sept},}


% micro + camera
@INPROCEEDINGS{1398362,
author={N. A. Fox and R. B. Reilly},
booktitle={Systems, Man and Cybernetics, 2004 IEEE International Conference on},
title={Robust multi-modal person identification with tolerance of facial expression},
year={2004},
volume={1},
pages={580-585 vol.1},
abstract={The research presented in This work describes audio-visual speaker identification experiments carried out on a large data set of 251 subjects. Both the audio and visual modeling is carried out using hidden Markov models. The visual modality uses the speaker's lip information. The audio and visual modalities are both degraded to emulate a train/test mismatch. The fusion method employed adapts automatically by using classifier score reliability estimates of both modalities to give improved audio-visual accuracies at all tested levels of audio and visual degradation, compared to the individual audio or visual modality accuracies. A maximum visual identification accuracy of 86\% was achieved. This result is comparable to the performance of systems using the entire face, and suggests the hypothesis that the system described would be tolerant to varying facial expression, since only the information around the speaker's lips is employed.},
keywords={face recognition;hidden Markov models;speech recognition;audio-visual speaker identification;facial expression;hidden Markov models;maximum visual identification;multimodal person identification;visual modality;Audio databases;Hidden Markov models;Identification of persons;Image databases;Signal processing;Visual databases},
doi={10.1109/ICSMC.2004.1398362},
ISSN={1062-922X},
month={Oct},}


% Keyboard
@INPROCEEDINGS{7435705,
author={S. Ravindran and C. Gautam and A. Tiwari},
booktitle={2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)},
title={Keystroke user recognition through extreme learning machine and evolving cluster method},
year={2015},
pages={1-5},
abstract={User Identification and User Verification are the primary problems in the area of Keystroke Dynamics. In the last decade there has been massive research in User Verification, and lesser research in User Identification. Both approaches take a username and a passphrase as input. In this paper, we introduce this problem of replacing authentication systems with the passphrase alone. This is done by using neural network based approach i.e. Extreme Learning Machine. ELM is a fast Single hidden layer feed forward network (SLFN) with good generalization performance. However the hidden layer in ELM does not have to be tuned. As an evolutionary step, we use a clustering based Semi-supervised approach (ECM-ELM) to User Recognition to combat variance in the accuracy of traditional ELMs. This research aims not only to address User Recognition problem but also to remove the instability in the accuracy of ELM. As per our simulation, ECM-ELM achieved a stable accuracy of 87\% with the CMU Keystroke Dataset, while ELM achieved an unstable average accuracy of 90\%.},
keywords={biometrics (access control);feedforward neural nets;learning (artificial intelligence);security of data;ELM;SLFN;evolving cluster method;extreme learning machine;keystroke dynamics;keystroke user recognition;neural network;replacing authentication systems;single hidden layer feed forward network;user identification;user verification;Authentication;Electronic countermeasures;Neural networks;Neurons;Testing;Training;ECM-ELM;Evolving Cluster Method;Extreme Learning Machine;Keystroke Biometrics;Keystroke Dynamics},
doi={10.1109/ICCIC.2015.7435705},
month={Dec},}


% Keyboard + face
@INPROCEEDINGS{7371386,
author={A. Gupta and A. Khanna and A. Jagetia and D. Sharma and S. Alekh and V. Choudhary},
booktitle={Computational Science and Engineering (CSE), 2015 IEEE 18th International Conference on},
title={Combining Keystroke Dynamics and Face Recognition for User Verification},
year={2015},
pages={294-299},
abstract={The massive explosion and ubiquity of computing devices and the outreach of the web have been the most defining events of the century so far. As more and more people gain access to the internet, traditional know-something and have-something authentication methods such as PINs and passwords are proving to be insufficient for prohibiting unauthorized access to increasingly personal data on the web. Therefore, the need of the hour is a user-verification system that is not only more reliable and secure, but also unobtrusive and minimalistic. Keystroke Dynamics is a novel Biometric Technique, it is not only unobtrusive, but also transparent and inexpensive. The fusion of keystroke dynamics and Face Recognition engenders the most desirable characteristics of a verification system. Our implementation uses Hidden Markov Models (HMM) for modeling the Keystroke Dynamics, with the help of two widely used Feature Vectors: Keypress Latency and Keypress Duration. On the other hand, Face Recognition makes use of the traditional Eigenfaces approach. The results show that the system has a high precision, with a False Acceptance Rate of 5.4\% and a False Rejection Rate of 9.2\%. Moreover, it is also future-proof, as the hardware requirements, i.e. camera and keyboard (physical or on-screen), have become an indispensable part of modern computing.},
keywords={Internet;authorisation;eigenvalues and eigenfunctions;face recognition;hidden Markov models;message authentication;HMM;Internet;PIN;World Wide Web;biometric technique;computing device;eigenfaces approach;face recognition;false acceptance rate;false rejection rate;feature vector;have-something authentication method;hidden Markov model;keypress duration;keypress latency;keystroke dynamics;know-something authentication method;password;personal data;unauthorized access;user verification;user-verification system;Face;Face recognition;Hardware;Heuristic algorithms;Hidden Markov models;Viterbi algorithm;Biometrics;Computer Security;Eigenfaces;Face Recognition;Hidden Markov Models;Keypress Duration;Keypress Latency;Keystroke Dynamics;Verification},
doi={10.1109/CSE.2015.37},
month={Oct},}

% clé materiel
@INPROCEEDINGS{1366012,
author={Y. Morozov and V. Sokil},
booktitle={Proceedings of the International Conference Modern Problems of Radio Engineering, Telecommunications and Computer Science, 2004.},
title={Hardware key for information systems users authentification},
year={2004},
pages={420-421},
abstract={The new hardware-software authentification complex of information systems and network users was developed. It supplements standard means of operational systems and raises resistance breakings. As the hardware key functions through the serial interface of the PC, such system work practically on any computer, including the built - in solutions. Taking into account low cost of introduction compared with other solutions and high reliability of functioning, such system can be used at operation of different information computer networks.},
keywords={certification;information networks;information systems;message authentication;hardware key function;hardware-software authentification;information computer networks;information systems;reliability;serial interface;Computer networks;Distributed computing;Finishing;Hardware;Information systems;Law;Permission;Production;Public key;Public key cryptography},
month={Feb},}

% comparatif
@ARTICLE{1246384,
author={L. O'Gorman},
journal={Proceedings of the IEEE},
title={Comparing passwords, tokens, and biometrics for user authentication},
year={2003},
volume={91},
number={12},
pages={2021-2040},
abstract={For decades, the password has been the standard means for user authentication on computers. However, as users are required to remember more, longer, and changing passwords, it is evident that a more convenient and secure solution to user authentication is necessary. This paper examines passwords, security tokens, and biometrics-which we collectively call authenticators-and compares these authenticators and their combinations. We examine their effectiveness against several attacks and suitability for particular security specifications such as compromise detection and nonrepudiation. Examples of authenticator combinations and protocols are described to show tradeoffs and solutions that meet chosen, practical requirements. The paper endeavors to offer a comprehensive picture of user authentication solutions for the purposes of evaluating options for use and identifying deficiencies requiring further research.},
keywords={biometrics (access control);identification technology;protocols;security;speaker recognition;access control;biometrics;end-user authentication;human authentication;identity management;identity token;passwords;verification;Authentication;Biometrics;Computer networks;Humans;Identity management systems;Internet;Protection;Protocols;Security;Web sites},
doi={10.1109/JPROC.2003.819611},
ISSN={0018-9219},
month={Dec},}

%Wifi
@ARTICLE{4801450,
author={M. L. Das},
journal={IEEE Transactions on Wireless Communications},
title={Two-factor user authentication in wireless sensor networks},
year={2009},
volume={8},
number={3},
pages={1086-1090},
abstract={Wireless sensor networks (WSN) are typically deployed in an unattended environment, where the legitimate users can login to the network and access data as and when demanded. Consequently, user authentication is a primary concern in this resource-constrained environment before accessing data from the sensor/gateway nodes. In this letter, we present a two-factor user authentication protocol for WSN, which provides strong authentication, session key establishment, and achieves efficiency.},
keywords={cryptographic protocols;internetworking;message authentication;wireless sensor networks;hash function;resource-constrained environment;sensor/gateway node;session key establishment;two-factor user authentication protocol;wireless sensor network;Access protocols;Authentication;Communication system security;Cryptography;Environmental economics;Monitoring;Power generation economics;Safety devices;Telecommunication traffic;Wireless sensor networks;Authentication;hash function;sensor networks;wireless security},
doi={10.1109/TWC.2008.080128},
ISSN={1536-1276},
month={March},}

% biométrique, mulimétrique
@ARTICLE{7723816,
author={A. Mosenia and S. SUR-KOLAY and A. Raghunathan and N. K. Jha},
journal={IEEE Transactions on Computers},
title={CABA: Continuous Authentication Based on BioAura},
year={2016},
volume={PP},
number={99},
pages={1-1},
abstract={Most computer systems authenticate users only once at the time of initial login, which can lead to security concerns. Continuous authentication has been explored as an approach for alleviating such concerns. Previous methods for continuous authentication primarily use biometrics, e.g., fingerprint and face recognition, or behaviometrics, e.g., key stroke patterns. We describe CABA, a novel continuous authentication system that is inspired by and leverages the emergence of sensors for pervasive and continuous health monitoring. CABA authenticates users based on their BioAura, an ensemble of biomedical signal streams that can be collected continuously and non-invasively using wearable medical devices. While each such signal may not be highly discriminative by itself, we demonstrate that a collection of such signals, along with robust machine learning, can provide high accuracy levels. We demonstrate the feasibility of CABA through analysis of traces from the MIMIC-II dataset. We propose various applications of CABA, and describe how it can be extended to user identification and adaptive access control authorization. Finally, we discuss possible attacks on the proposed scheme and suggest corresponding countermeasures.},
keywords={Authentication;Authorization;Biomedical monitoring;Biometrics (access control);Monitoring;Scalability;Authentication;behaviometrics;biomedical signals;biometrics;biostreams;continuous authentication;machine learning;security;wearable medical devices},
doi={10.1109/TC.2016.2622262},
ISSN={0018-9340},
month={},}

@ARTICLE{anssi_auth,
author={Cellule d\'Assistance Technique en Conception de la DCSSI},
journal={IEEE Transactions on Computers},
title={Authentification Règles et recommandations concernant les mécanismes d'authentification de niveau de robustesse standard},
year={2007},
doi={729/SGDN/DCSSI/SDS/AsTeC},
ISSN={0018-9340},
month={},}


% Black Hat bypass
@article{duc2009your,
  title={Your face is not your password face authentication bypassing lenovo--asus--toshiba},
  author={Duc, Nguyen Minh and Minh, Bui Quang},
  journal={Black Hat Briefings},
  year={2009}
}

% Anomaly detection
@inproceedings{lane1997sequence,
  title={Sequence matching and learning in anomaly detection for computer security},
  author={Lane, Terran and Brodley, Carla E and others},
  booktitle={AAAI Workshop: AI Approaches to Fraud Detection and Risk Management},
  pages={43--49},
  year={1997}
}

@article{hodge2004survey,
  title={A survey of outlier detection methodologies},
  author={Hodge, Victoria J and Austin, Jim},
  journal={Artificial intelligence review},
  volume={22},
  number={2},
  pages={85--126},
  year={2004},
  publisher={Springer}
}

@article{chandola2009anomaly,
  title={Anomaly detection: A survey},
  author={Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
  journal={ACM computing surveys (CSUR)},
  volume={41},
  number={3},
  pages={15},
  year={2009},
  publisher={ACM}
}

% gpu cracking
@INPROCEEDINGS{5665047,
author={T. Murakami and R. Kasahara and T. Saito},
booktitle={2010 10th International Symposium on Communications and Information Technologies},
title={An implementation and its evaluation of password cracking tool parallelized on GPGPU},
year={2010},
pages={534-538},
abstract={General-purpose computing on graphics processing units (GPGPU) is popular computing technology to utilize in various fields. In the paper, we parallelize cryptographical hash processing of a password cracking tool, John the Ripper, by utilizing CUDA on GPGPU. We also evaluate our work to compare the processing time of hash processing parallelized by GPU with that of the John the Ripper on a dual-core CPU. Processing time of our implementation is 0.03\% of that of the original one.},
keywords={computer graphics;cryptography;CUDA;GPGPU;John the Ripper;cryptographical hash processing;general-purpose computing on graphics processing unit;password cracking tool;Central Processing Unit;Dictionaries;Graphics processing unit;Instruction sets;Linux;Random access memory;Registers},
doi={10.1109/ISCIT.2010.5665047},
month={Oct},}

% Cloud cracking
@inproceedings{roth2011breaking,
  title={Breaking encryptions using GPU accelerated cloud instances},
  author={Roth, Thomas},
  booktitle={Black Hat Technical Security Conference},
  year={2011}
}

% Keystroking
@article{brown1993user,
  title={User identification via keystroke characteristics of typed names using neural networks},
  author={Brown, Marcus and Rogers, Samuel Joe},
  journal={International Journal of Man-Machine Studies},
  volume={39},
  number={6},
  pages={999--1014},
  year={1993},
  publisher={Elsevier}
}

% Expansion du Wifi
@misc{wifialliance,
  title={Wi-Fi Alliance\textregistered introduces low power, long range Wi-Fi HaLow\texttrademark},
  year={2016},
  author={Wi-Fi Alliance},
  url = {https://www.wi-fi.org/news-events/newsroom/wi-fi-alliance-introduces-low-power-long-range-wi-fi-halow},
urldate = {2016-12-01}
}

% Reconnaisance de lieu par le wifi
@article{1611.02049v1,
  title={Low-effort place recognition with WiFifingerprints using deep learning},
  author = {Nowicki, Micha{\textbackslash}l and Wietrzykowski, Jan},
  place={Institute of Control and Information Engineering,Poznań University of Technology},
  abstract={Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions},
  pages={10},
  year={2016},
  journal={arXiv preprint arXiv:1611.02049}
}

% reconnaissance d'humain par le wifi
@article{1608.03430,
  title={FreeSense:Indoor Human Identification with WiFi Signals},
  author={Tong Xin, Bin Guo, Zhu Wang, Mingyang Li, Zhiwen Yu},
  place={School of Computer Science Northwestern Polytechnical University Xi\’an, P. R. China},
  abstract={Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages WIFI signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding WIFI signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of WIFI. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform-based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9 to 94.5 per cent when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.},
  pages={6},
  year={2016},
  journal={arXiv preprint arXiv:1608.03430}
}

% face recognisation
@article{phillips2007frvt,
  title={FRVT 2006 and ICE 2006 large-scale results},
  author={Phillips, P Jonathon and Scruggs, W Todd and O’Toole, Alice J and Flynn, Patrick J and Bowyer, Kevin W and Schott, Cathy L and Sharpe, Matthew},
  journal={National Institute of Standards and Technology, NISTIR},
  volume={7408},
  pages={1},
  year={2007}
}


% lip identification
@inproceedings{gomez_biometric_2002,
	title = {Biometric identification system by lip shape},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1049223},
	urldate = {2016-12-04},
	booktitle = {Security {Technology}, 2002. {Proceedings}. 36th {Annual} 2002 {International} {Carnahan} {Conference} on},
	publisher = {IEEE},
	author = {Gomez, Enrique and Travieso, Carlos M. and Briceno, J. C. and Ferrer, M. A.},
	year = {2002},
	pages = {39--42},
}

% iris
@article{vatsa2008improving,
  title={Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing},
  author={Vatsa, Mayank and Singh, Richa and Noore, Afzel},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
  volume={38},
  number={4},
  pages={1021--1035},
  year={2008},
  publisher={IEEE}
}

% face recognition
@article{hsu2002face,
  title={Face detection in color images},
  author={Hsu, Rein-Lien and Abdel-Mottaleb, Mohamed and Jain, Anil K},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  volume={24},
  number={5},
  pages={696--706},
  year={2002},
  publisher={IEEE}
}

% 11/12
@inproceedings{turk1991face,
  title={Face recognition using eigenfaces},
  author={Turk, Matthew A and Pentland, Alex P},
  booktitle={Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on},
  pages={586--591},
  year={1991},
  organization={IEEE}
}

@incollection{zhao1998discriminant,
  title={Discriminant analysis of principal components for face recognition},
  author={Zhao, Wenyi and Krishnaswamy, Arvindh and Chellappa, Rama and Swets, Daniel L and Weng, John},
  booktitle={Face Recognition},
  pages={73--85},
  year={1998},
  publisher={Springer}
}


@article{schneier2005two,
  title={Two-factor authentication: too little, too late.},
  author={Schneier, Bruce},
  journal={Commun. ACM},
  volume={48},
  number={4},
  pages={136},
  year={2005}
}

@inproceedings{aloul2009two,
  title={Two factor authentication using mobile phones.},
  author={Aloul, Fadi A and Zahidi, Syed and El-Hajj, Wassim},
  booktitle={AICCSA},
  pages={641--644},
  year={2009}
}

@inproceedings{wu2004secure,
  title={Secure web authentication with mobile phones},
  author={Wu, Min and Garfinkel, Simson and Miller, Rob},
  booktitle={DIMACS workshop on usable privacy and security software},
  volume={2010},
  year={2004}
}


@INPROCEEDINGS{6507505,
author={D. Apostal and K. Foerster and A. Chatterjee and T. Desell},
booktitle={2012 19th International Conference on High Performance Computing},
title={Password recovery using MPI and CUDA},
year={2012},
pages={1-9},
keywords={application program interfaces;database management systems;graphics processing units;message passing;parallel architectures;security of data;storage management;CUDA;GPU computing;GPU devices;GPU memory utilization;HPC framework;MPI nodes;communication latency;compute unified device architecture;computer system security;course-grained division;dictionary-based password recovery algorithms;divided dictionary algorithm;divided password database algorithm;electronic authentication;fine-grained division;hash function;hash values;high level fine-grained parallelism;high performance computing;message passing interface;minimal memory algorithm;string comparison operation;unknown user passwords;user identity verification},
doi={10.1109/HiPC.2012.6507505},
month={Dec},}