Keystroke dynamics Based Technique to Enhance the Security in Smart Devices

Keystroke dynamics Based Technique to Enhance the Security in Smart Devices


  • Farman Pirzado Mohammad Ali Jinnah University, Karachi, Sindh, Pakistan
  • Shahzad Memon Faculty of Engineering and Technology University of Sindh Jamshoro, Pakistan
  • Lachman Das Dhomeja Faculty of Engineering and Technology University of Sindh Jamshoro, Pakistan
  • Awais Ahmed Department of Computer Science Mohammad Ali Jinnah University Karachi, Pakistan



Keystroke dynamics, Smart Devices, user authentication


Nowadays, smart devices have become a part of our
lives, hold our data, and are used for sensitive transactions like
internet banking, mobile banking, etc. Therefore, it is crucial to
secure the data in these smart devices from theft or misplacement.
The majority of the devices are secured with password/PINbased
user authentication methods, which are already proved
a less secure or easily guessable user authentication method.
An alternative technique for securing smart devices is keystroke
dynamics. Keystroke dynamics (KSD) is behavioral biometrics,
which uses a natural typing pattern unique in every individual
and difficult to fake or replicates that pattern. This paper
proposes a user authentication model based on KSD as an additional
security method for increasing the smart devices’ security
level. In order to analyze the proposed model, an android-based
application has been implemented for collecting data from fake
and genuine users. Six machine learning algorithms have been
tested on the collected data set to study their suitability for use
in the keystroke dynamics-based authentication model.


M Karnan and N Krishnaraj. A model to secure mobile devices using

keystroke dynamics through soft computing techniques. International

Journal of Soft Computing and Engineering (IJSCE) ISSN, pages 2231–

, 2012.

Mohd Anwar and Ashiq Imran. A comparative study of graphical and

alphanumeric passwords for mobile device authentication. In MAICS,

pages 13–18, 2015.

Asma Salem, Ahmad Sharieh, Azzam Sleit, and Riad Jabri. Enhanced

authentication system performance based on keystroke dynamics using

classification algorithms. KSII Transactions on Internet & Information

Systems, 13(8), 2019.

Anil K Jain, Karthik Nandakumar, and Abhishek Nagar. Biometric

template security. EURASIP Journal on advances in signal processing,

:1–17, 2008.

Yu Zhong and Yunbin Deng. A survey on keystroke dynamics biometrics:

approaches, advances, and evaluations. Recent Advances in User

Authentication Using Keystroke Dynamics Biometrics, pages 1–22, 2015.

Himanka Kalita, Emanuele Maiorana, and Patrizio Campisi. Keystroke

dynamics for biometric recognition in handheld devices. In 2020 43rd

International Conference on Telecommunications and Signal Processing

(TSP), pages 410–416. IEEE, 2020.

Dong In Kim, Shincheol Lee, and Ji Sun Shin. A new feature scoring

method in keystroke dynamics-based user authentications. IEEE Access,

:27901–27914, 2020.

Anbiao Huang, Shuo Gao, Junliang Chen, Lijun Xu, and Arokia Nathan.

High security user authentication enabled by piezoelectric keystroke

dynamics and machine learning. IEEE Sensors Journal, 2020.

Baljit Singh Saini, Parminder Singh, Anand Nayyar, Navdeep Kaur,

Kamaljit Singh Bhatia, Shaker El-Sappagh, and Jong-Wan Hu. A

three-step authentication model for mobile phone user using keystroke

dynamics. IEEE Access, 8:125909–125922, 2020.

Shri Kant, Alok Katiyar, and Shubhii Shuklla. Smart mobile device

authentication using keystroke dynamics based behavior classification.

Emanuele Maiorana, Himanka Kalita, and Patrizio Campisi. Deepkey:

Keystroke dynamics and cnn for biometric recognition on mobile devices.

In 2019 8th European Workshop on Visual Information Processing

(EUVIP), pages 181–186. IEEE, 2019.

Asma Salem and Mohammad S Obaidat. A novel security scheme for

behavioral authentication systems based on keystroke dynamics. Security

and Privacy, 2(2):e64, 2019.

Hayreddin C¸ eker and Shambhu Upadhyaya. User authentication with

keystroke dynamics in long-text data. In 2016 IEEE 8th International

Conference on Biometrics Theory, Applications and Systems (BTAS),

pages 1–6. IEEE, 2016.

Ricardo N Rodrigues, Glauco FG Yared, Carlos R do N Costa, Jo˜ao BT

Yabu-Uti, F´abio Violaro, and Lee Luan Ling. Biometric access control

through numerical keyboards based on keystroke dynamics. In International

Conference on Biometrics, pages 640–646. Springer, 2006.

Naveed Riaz, Ayesha Riaz, and Sajid Ali Khan. Biometric template

security: an overview. Sensor Review, 2018.

Taiwo Oladipupo Ayodele. Types of machine learning algorithms. New

advances in machine learning, 3:19–48, 2010.

Ayon Dey. Machine learning algorithms: a review. International Journal

of Computer Science and Information Technologies, 7(3):1174–1179,

T Pandikumar, Abraham Fekede, and Capt Zinabu Haile. Enhancing

performance and usability of keystroke dynamics authentication on mobile

touchscreen devices using features extraction scheme. International

Journal of Engineering Science, 13415, 2017.

Amund Tveit, Magnus Lie Hetland, and H°aavard Engum. Incremental

and decremental proximal support vector classification using decay

coefficients. In International Conference on Data Warehousing and

Knowledge Discovery, pages 422–429. Springer, 2003.