A Machine Learning Method For Sensor Authentication Using Hidden Markov Models

被引：0
|
作者：
Murphy, Julian [1 ,2 ]
Howells, Gareth [1 ]
McDonald-Maier, Klaus D. [2 ]
机构：
[1] Univ Kent, Sch Engn & Digital Arts, Canterbury, New Zealand
[2] Univ Essex, Sch Comp Sci & Elect Engn, Colchester, Essex, England
来源：
2019 EIGHTH INTERNATIONAL CONFERENCE ON EMERGING SECURITY TECHNOLOGIES (EST) | 2019年
基金：
英国工程与自然科学研究理事会;
关键词：
PROBABILISTIC FUNCTIONS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A machine learning method for sensor based authentication is presented. It exploits hidden markov models to generate stable and synthetic probability density functions from variant sensor data. The principle, and novelty, of the new method are presented in detail together with a statistical evaluation. The results show a marked improvement in stability through the use of hidden markov models.
引用
收藏
页数：5
相关论文
共 50 条
  • [1] Probabilistic face authentication using Hidden Markov Models
    Bicego, M
    Grosso, E
    Tistarelli, M
    BIOMETRIC TECHNOLOGY FOR HUMAN IDENTIFICATION II, 2005, 5779 : 299 - 306
  • [2] Machine Learning-Based Attack Detection for Wireless Sensor Network Security Using Hidden Markov Models
    Affane, M. Anselme R.
    Satori, Hassan
    Boutazart, Youssef
    Ezzine, Abderahim
    Satori, Khalid
    WIRELESS PERSONAL COMMUNICATIONS, 2024, 135 (04) : 1965 - 1992
  • [3] Predicting the phosphorylation sites using hidden Markov models and machine learning methods
    Senawongse, P
    Dalby, AR
    Yang, ZR
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2005, 45 (04) : 1147 - 1152
  • [4] A learning method of hidden Markov models for sequence discrimination
    Mamitsuka, H
    JOURNAL OF COMPUTATIONAL BIOLOGY, 1996, 3 (03) : 361 - 373
  • [5] Enhancing speaker authentication systems using circular hidden Markov models
    Shahin, I
    ISSPA 2005: The 8th International Symposium on Signal Processing and its Applications, Vols 1 and 2, Proceedings, 2005, : 703 - 706
  • [6] Continuous Operator Authentication for Teleoperated Systems Using Hidden Markov Models
    Yan, Junjie
    Huang, Kevin
    Lindgren, Kyle
    Bonaci, Tamara
    Chizeck, Howard J.
    ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS, 2022, 6 (01)
  • [7] Place learning and recognition using hidden Markov models
    Aycard, O
    Charpillet, F
    Fohr, D
    Mari, JF
    IROS '97 - PROCEEDINGS OF THE 1997 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOT AND SYSTEMS: INNOVATIVE ROBOTICS FOR REAL-WORLD APPLICATIONS, VOLS 1-3, 1996, : 1741 - 1746
  • [8] Learning Hidden Markov Models Using Conditional Samples
    Kakade, Sham
    Krishnamurthy, Akshay
    Mahajan, Gaurav
    Zhang, Cyril
    THIRTY SIXTH ANNUAL CONFERENCE ON LEARNING THEORY, VOL 195, 2023, 195
  • [9] Semisupervised Learning of Hidden Markov Models via a Homotopy Method
    Ji, Shihao
    Watson, Layne T.
    Carin, Lawrence
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2009, 31 (02) : 275 - 287
  • [10] Learning Hidden Markov Sparse Models
    Li, Lin
    Scaglione, Anna
    2013 INFORMATION THEORY AND APPLICATIONS WORKSHOP (ITA), 2013,
12345