GA SVM Wrapper Ensemble for Keystroke Dynamics Authentication

Sung, Ki-Seok; Cho, Sungzoon

doi:10.1007/11608288_87

Cited by 26 publications

(14 citation statements)

References 8 publications

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“…SVM, an excellent novelty detector with fast learning speed, is employed as a base learner. Ki-seok Sung and Sungzoon Cho [44] proposed a one step approach similar to that of Genetic Ensemble Feature Selection (GEFS). They used SVM as base classifier for classification similar to that of Yu and Cho [43].…”

Section: Feature Subset Selection Methodsmentioning

confidence: 99%

“…Method Remarks 1 GA -SVM with Gaussian Kernal [42,43] The degree of diversity and quality are guaranteed, and thus they gave result in an improved model performance and stability 2 GA -SVM wrapper ensemble [44] It reports an average FAR of 15.78% with minimum FAR of 5.3% and maximum FAR of 20.38% for raw data with noise 3 GA -PSO [45,46] Standard GA and PSO variation was used and produced a good result for the tasks of feature selection and personal identification with an FAR of 0.81% and IPR of 0.76% 4…”

Section: Slnomentioning

confidence: 99%

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Biometric personal authentication using keystroke dynamics: A review

Karnan¹,

Akila²,

Krishnaraj³

2011

Applied Soft Computing

205

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Section: Feature Subset Selection Methodsmentioning

confidence: 99%

Section: Slnomentioning

confidence: 99%

Biometric personal authentication using keystroke dynamics: A review

Karnan¹,

Akila²,

Krishnaraj³

2011

Applied Soft Computing

205

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“…We will briefly describe some of these approaches from [3,4,5,6,7]; a more detailed overview is given in [8].…”

Section: Existing Approachesmentioning

confidence: 99%

“…Key press durations (dwell times) are used as a model in [6], where they are calculated by three different techniques. The learning algorithm is the multiclass linear SVM [7], because it demonstrates the best performance on simple data structures. The test subjects were divided into two groups for data collection: one group was aware of the ongoing experiment, the other not.…”

Section: Use Of Right and Left Shiftmentioning

confidence: 99%

Machine Learning Methods in Authentication Problems Using Password Keystroke Dynamics

et al. 2015

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We examine the problem of user authentication from keystroke dynamics. A new static authentication method that collects information about user keystrokes is described. Its applicability to the authentication problem is substantiated by experiments and the optimal conditions for the implementation of the method are chosen.In most modern information systems, one of the main issues, alongside with data storage and processing, is the granting of access permissions. Permissions prevent unauthorized access by outsiders and also differentiate between the access rights of staff members within the organization. The authentication problem, i.e., verifying the legitimacy of the user seeking to access the system, is thus one of the key problems in information systems.Various methods are available to solve this problem. The most popular technique used in modern information system is password authentication -authentication by a special string of symbols known only to the user who has permission to access the system. In addition to obvious advantages -passwords are simple to implement and their use is very widespread, password authentication suffers from essential shortcomings: the user may forget the code string or reveal it to other users; if the password is too short or too simple, it can be found by direct enumeration or by dictionary-based enumeration. All this casts serious doubts on password use in high-security systems.Authentication by means of electronic and digital signatures (EDS) is free from the last shortcoming [1]. However, it is impossible to ensure secure storage of closed keys in this way, because they are stored using other access control tools.For these reasons, many effective security systems rely on biometrics to identify the user. Biometric systems fall in two categories. One category includes systems using features that differ for natural reasons among different individuals: fingerprints, retina scans, voice patterns, body temperature chart, etc. These systems are effective but quite costly because they require special equipment. The second category includes systems that analyze user behavior; they are based on user experience and skills. They do not require any special equipment and are simple to introduce. One of such systems analyzes user keystroke dynamics.

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“…Some of the relevant work on user authentication system has been reported in [3], [9] and [8]. However only [10] has done some preliminary work using GA. To the best of our knowledge no previous study has used any of the genetic algorithms for user authentication on smart phones.…”

Section: Related Workmentioning

confidence: 99%

A hybrid GA-PSO fuzzy system for user identification on smart phones

Shahzad

Zahid

Farooq

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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The major contribution of this paper is a hybrid GA-PSO fuzzy user identification system, UGuard, for smart phones. Our system gets 3 phone usage features as input to identify a user or an imposter. We show that these phone usage features for different users are diffused; therefore, we justify the need of a front end fuzzy classifier for them. We further show that the fuzzy classifier must be optimized using a back end online dynamic optimizer. The dynamic optimizer is a hybrid of Particle Swarm Optimizer (PSO) and Genetic Algorithm (GA). We have collected phone usage data of 10 real users having Symbian smart phones for 8 days. We evaluate our UGuard system on this dataset. The results of our experiments show that UGuard provides on the average an error rate of 2% or less. We also compared our system with four classical classifiers -Naïve Bayes, Back Propagation Neural Networks, J48 Decision Tree, and Fuzzy System -and three evolutionary schemes -fuzzy system optimized by ACO, PSO, and GA. To the best of our knowledge, the current work is the first system that has achieved such a small error rate. Moreover, the system is simple and efficient; therefore, it can be deployed on real world smart phones.

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GA SVM Wrapper Ensemble for Keystroke Dynamics Authentication

Cited by 26 publications

References 8 publications

Biometric personal authentication using keystroke dynamics: A review

Biometric personal authentication using keystroke dynamics: A review

Machine Learning Methods in Authentication Problems Using Password Keystroke Dynamics

A hybrid GA-PSO fuzzy system for user identification on smart phones

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