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The BeiHang Keystroke Dynamics Authentication System  [PDF]
Juan Liu,Baochang Zhang,Linlin Shen,Jianzhuang Liu,Jason Zhao
Computer Science , 2013,
Abstract: Keystroke Dynamics is an important biometric solution for person authentication. Based upon keystroke dynamics, this paper designs an embedded password protection device, develops an online system, collects two public databases for promoting the research on keystroke authentication, exploits the Gabor filter bank to characterize keystroke dynamics, and provides benchmark results of three popular classification algorithms, one-class support vector machine, Gaussian classifier, and nearest neighbour classifier.
Keystroke Dynamics Based Authentication Using Information Sets  [PDF]
Aparna Bhatia, Madasu Hanmandlu
Journal of Modern Physics (JMP) , 2017, DOI: 10.4236/jmp.2017.89094
Abstract: This paper presents keystroke dynamics based authentication system using the information set concept. Two types of membership functions (MFs) are computed: one based on the timing features of all the samples and another based on the timing features of a single sample. These MFs lead to two types of information components (spatial and temporal) which are concatenated and modified to produce different feature types. Two Component Information Set (TCIS) is proposed for keystroke dynamics based user authentication. The keystroke features are converted into TCIS features which are then classified by SVM, Random Forest and proposed Convex Entropy Based Hanman Classifier. The TCIS features are capable of representing the spatial and temporal uncertainties. The performance of the proposed features is tested on CMU benchmark dataset in terms of error rates (FAR, FRR, EER) and accuracy of the features. In addition, the proposed features are also tested on Android Touch screen based Mobile Keystroke Dataset. The TCIS features improve the performance and give lower error rates and better accuracy than that of the existing features in literature.
Keystroke Dynamics Authentication For Collaborative Systems  [PDF]
Romain Giot,Mohamad El-Abed,Christophe Rosenberger
Computer Science , 2009, DOI: 10.1109/CTS.2009.5067478
Abstract: We present in this paper a study on the ability and the benefits of using a keystroke dynamics authentication method for collaborative systems. Authentication is a challenging issue in order to guarantee the security of use of collaborative systems during the access control step. Many solutions exist in the state of the art such as the use of one time passwords or smart-cards. We focus in this paper on biometric based solutions that do not necessitate any additional sensor. Keystroke dynamics is an interesting solution as it uses only the keyboard and is invisible for users. Many methods have been published in this field. We make a comparative study of many of them considering the operational constraints of use for collaborative systems.
Keystroke Authentication on Enhanced Needleman Alignment Algorithm  [PDF]
Seham Bamatraf, Mohamed Bamatraf, Osman Hegazy
Intelligent Information Management (IIM) , 2014, DOI: 10.4236/iim.2014.64021
Abstract:

An important point for computer systems is the identification of users for authentication. One of these identification methods is keystroke dynamics. The keystroke dynamics is a biometric measurement in terms of keystroke press duration and keystroke latency. However, several problems are arisen like the similarity between users and identification accuracy. In this paper, we propose innovative model that can help to solve the problem of similar user by classifying user’s data based on a membership function. Next, we employ sequence alignment as a way of pattern discovery from the user’s typing behaviour. Experiments were conducted to evaluate accuracy of the proposed model. The results show high performance compared to standard classifiers in terms of accuracy and precision.

Profile Generation Methods for Reinforcing Robustness of Keystroke Authentication in Free Text Typing  [PDF]
Yoshitomo Matsubara, Toshiharu Samura, Haruhiko Nishimura
Journal of Information Security (JIS) , 2015, DOI: 10.4236/jis.2015.62014
Abstract: We have investigated several characteristics of the keystroke authentication in Japanese free text typing, and our methods have provided high recognition accuracy for high typing skill users who can type 700 or more letters per 5 minutes. There are, however, some situations decreasing the accuracy such as long period passage after registering each user’s profile documents and existence of lower typing skill users who can type only about 500 - 600 letters per 5 minutes. In this paper, we propose new profile generation methods, profile-updating and profile-combining methods, to reinforce the robustness of keystroke authentication and show the effectiveness of them through three examinations with experimental data.
Keystroke Dynamics User Authentication Based on Gaussian Mixture Model and Deep Belief Nets  [PDF]
Yunbin Deng,Yu Zhong
ISRN Signal Processing , 2013, DOI: 10.1155/2013/565183
Abstract: User authentication using keystroke dynamics offers many advances in the domain of cyber security, including no extra hardware cost, continuous monitoring, and nonintrusiveness. Many algorithms have been proposed in the literature. Here, we introduce two new algorithms to the domain: the Gaussian mixture model with the universal background model (GMM-UBM) and the deep belief nets (DBN). Unlike most existing approaches, which only use genuine users’ data at training time, these two generative model-based approaches leverage data from background users to enhance the model’s discriminative capability without seeing the imposter’s data at training time. These two new algorithms make no assumption about the underlying probability distribution and are fast for training and testing. They can also be extended to free text use cases. Evaluations on the CMU keystroke dynamics benchmark dataset show over 58% reduction in the equal error rate over the best published approaches. 1. Introduction With the ever increasing demand for more secure access control in many of today’s security applications, traditional methods fail to keep up with the challenges because pins, tokens, and passwords are too many to remember. Even carefully crafted user name and password can be hacked, which compromises the system security. On the other hand, biometrics [1–5] based on “who” the person is or “how” the person acts, as compared with what the person has (key) and knows (password), presents a significant security advancement to meet these new challenges. Among them, keystroke dynamics [6] provides a natural choice for secure “password-free” computer access with no additional hardware required. Keystroke dynamics refers to the habitual patterns or rhythms an individual exhibits while typing on a keyboard input device. These rhythms and patterns of tapping are idiosyncratic, [7] the same way as handwritings or signatures are, due to their similar governing neurophysiological mechanisms. Back in the 19th century, telegraph operators could recognize each other based on one’s specific tapping style [8]. Recently, it is shown that typing text can be deciphered simply based on the sound of key typing [9]. As such, it is believed that the keystroke dynamics contains enough information to be a good biometrics to ascertain a user at the keyboard. Compared with other biometrics, keystroke biometrics has additional attractiveness for its user-friendliness and nonintrusiveness. Keystroke dynamics data can be collected without a user’s awareness. Continuous authentication is possible using
Application of a Dynamic Identity Authentication Model Based on an Improved Keystroke Rhythm Algorithm  [PDF]
Wenchuan YANG, Fang FANG
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2009, DOI: 10.4236/ijcns.2009.28082
Abstract: Keystroke rhythm identification, which extracts biometric characteristics through keyboards without addi-tional expensive devices, is a kind of biometric identification technology. The paper proposes a dynamic identity authentication model based on the improved keystroke rhythm algorithm in Rick Joyce model and implement this model in a mobile phone system. The experimental results show that comparing with the original model, the false alarm rate (FAR) of the improved model decreases a lot in the mobile phone system, and its growth of imposter pass rate (IPR) is slower than the Rick Joyce model’s. The improved model is more suitable for small memory systems, and it has better performance in security and dynamic adaptation. This improved model has good application value.
An Anomaly Detector for Keystroke Dynamics Based on Medians Vector Proximity  [PDF]
Mudhafar M. Al-Jarrah
Journal of Emerging Trends in Computing and Information Sciences , 2012,
Abstract: This paper presents an anomaly detector for keystroke dynamics authentication, based on a statistical measure of proximity, evaluated through the empirical study of an independent benchmark of keystroke data. A password typing-rhythm classifier is presented, to be used as an anomaly detector in the authentication process of genuine users and impostors. The proposed user authentication method involves two phases. First a training phase in which a user typing profile is created through repeated entry of password. In the testing phase, the password typing rhythm of the user is compared with the stored typing profile, to determine whether it is a genuine user or an impostor. The typing rhythm is obtained through keystroke timings of key-down / key-up of individual keys and the latency between keys. The training data is stored as a typing profile, consisting of a vector of median values of elements of the feature set, and as a vector of standard deviations for the same elements. The proposed classifier algorithm computes a score for the typing of a password to determine authenticity. A measure of proximity is used in the comparison between feature set medians vector and feature set testing vector. Each feature in the testing vector is given a binary score of 1 if it is within a proximity distance threshold from the stored median of that feature, otherwise the score is 0. The proximity distance threshold for a feature is chosen to be the standard deviation of that feature in the training data. The typing of a password is classified as genuine if the accumulated score for all features meet a minimum acceptance threshold. Analysis of the benchmark dataset using the proposed classifier has given an improved anomaly detection performance in comparison with results of 14 algorithms that were previously tested using the same benchmark.
User authentication algorithm with keystroke features based on genetic algorithms and grey relational analysis
基于遗传算法和灰色关联分析的击键特征识别算法

WANG Xuan,CHEN Wei-wei,MA Jian-feng,
王晅
,陈伟伟,马建峰

计算机应用 , 2007,
Abstract: User authentication based on keystroke dynamics features is more secure than conventional user authentication approach only based on passwords.The neural network and data mining-based methods present high authentication accuracy,but have a high computational cost.The statistical and vector-based methods have shown low computational complexity,but are less accurate in user authentication.In order to improve authentication accuracy and reduce computational complexity synchronously,a new user authentication approach based on keystroke patterns was proposed.In the proposed approach,Genetic algorithm was employed to generate the common keystroke pattern of each user from the training set consisting of the user's normal keystroke samples.Then Grey Relational analysis method was applied to calculate the degree of grey slope incidence between common keystroke pattern and current keystroke pattern,the resultant value was compared with a threshold value determined by experiment to implement user authentication.Experimental results show this approach represents the same user authentication accuracy as neural network and data mining-based methods in terms of False Acceptance Rate(FAR) and False Rejection Rate(FRR),false acceptance rate and false rejection rate of this method are 1.5% and 0% respectively.It is also shows that the computational complexity of the proposed method is lower than that of some other methods.
基于CNN和RNN的自由文本击键模式持续身份认证
Free-text keystroke continuous authentication using CNN and RNN
 [PDF]

芦效峰,张胜飞,伊胜伟
- , 2018, DOI: 10.16511/j.cnki.qhdxxb.2018.26.048
Abstract: 个人击键节奏模式具有很难被模仿的特点并可以用于身份认证。根据个人自由文本输入时的击键数据可以学习到个人独有的击键模式。基于对用户自由文本击键输入的检测,能够在不影响用户输入的情况下完成对用户身份的持续认证。该文提出将整体击键数据划分成固定长度的击键序列,并且根据击键的时间特征将击键序列中的击键时间数据转化成击键向量。使用卷积神经网络(convolutional neural networks,CNN)加循环神经网络(recurrent neural networks,RNN)的模型进行个人击键向量序列进行学习,用于身份认证。结果表明:模型使用公开数据集进行实验获得最优拒真率(false rejection rate,FRR)为1.95%,容假率(false acceptance rate,FAR)为4.12%,相等错误率(equal error rate,EER)为3.04%。
Abstract:Personal keystroke input patterns are difficult to imitate and can be used for identity authentication. The personal keystroke input data for a free-text can be used to learn the unique keystroke mode of a person. Detection based on a user's free-text keystrokes can be used for continuous identity authentication without affecting the user input. This paper presents a model that divides the keystroke data into fixed-length keystroke sequences and converts the keystroke time data in the keystroke sequence into a keystroke vector according to the time characteristics of the keystrokes. A convolutional neural network and a recurrent neural network are then used to learn the sequences of the personal keystroke vectors for identity authentication. The model was tested on an open data set with an optimal false rejection rate (FRR) of 1.95%, a false acceptance rate (FAR) of 4.12%, and an equal error rate (EER) of 3.04%.
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