Abstract
The generative model and discriminative model are the two categories of statistical models used in keystroke biometric areas. Generative models have the trait of handling missing or irregular data, and perform well for limited training data. Discriminative models are fast in making predictions for new data, resulting in faster classification of new data compared to the generative models. In an attempt to build an efficient model for keystroke biometric user identification, this study proposes a hybrid POHMM/SVM method taking advantage of both generative and discriminative models. The partially observable hidden Markov model (POHMM) is an extension of the hidden Markov model (HMM), which has shown promising performance in user verification and handling missing or infrequent data. On the other hand, the support vector machine (SVM) has been a widely used discriminative model in keystroke biometric systems for the last decade and achieved a higher accuracy rate for large data sets. In the proposed model, features are extracted using the POHMM model, and a one-class support vector machine is used as the anomaly detector. For user identification, the study examines POHMM parameters using five different discriminative classifiers: support vector machines, k-nearest neighbor, random forest, multilayer perceptron (MLP) neural network, and logistic regression. The best accuracy of 91.3% (mean 0.868, SD 0.132) is achieved by the proposed hybrid POHMM/SVM approach among all generative and discriminative models.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference74 articles.
1. Identity authentication based on keystroke latencies
2. Biometric studies with hidden Markov model and its extension on short fixed-text input;Ali;Proceedings of the 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON),2017
3. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes;Ng;Adv. Neural Inf. Process. Syst.,2001
4. Information Processing in Dynamical Systems: Foundations of Harmony Theory;Smolensky,1986
5. Discriminative Models, not Discriminative Training;Minka,2005
Cited by
19 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Analysis of various machine learning models in detecting credit card fraud activities;Fifth International Conference on Computer Vision and Computational Intelligence (CVCI 2024);2024-05-27
2. Secure and Robust User Authentication Using Transfer Learning and CTGAN-based Keystroke Dynamics;2024 IEEE 3rd International Conference on Computing and Machine Intelligence (ICMI);2024-04-13
3. Machine Learning Algorithms for Diabetes Diagnosis Prediction;2024 6th International Conference on Image, Video and Signal Processing;2024-03-14
4. An Overview of Joint Biometric Identification for Secure Online Voting with Blockchain Technology;2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA);2023-12-21
5. Traffic Flow Analysis in Digital Forensics: Unveiling Patterns and Anomalies;2023 7th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS);2023-11-02