CFP last date
15 October 2024
Call for Paper
November Edition
IJAIS solicits high quality original research papers for the upcoming November edition of the journal. The last date of research paper submission is 15 October 2024

Submit your paper
Know more
Reseach Article

Machine Learning for User-Authentication on Mobile Devices using Typing Patterns

by Odeniyi O.A.
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 44
Year of Publication: 2024
Authors: Odeniyi O.A.
10.5120/ijais2024451971

Odeniyi O.A. . Machine Learning for User-Authentication on Mobile Devices using Typing Patterns. International Journal of Applied Information Systems. 12, 44 ( May 2024), 17-21. DOI=10.5120/ijais2024451971

@article{ 10.5120/ijais2024451971,
author = { Odeniyi O.A. },
title = { Machine Learning for User-Authentication on Mobile Devices using Typing Patterns },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2024 },
volume = { 12 },
number = { 44 },
month = { May },
year = { 2024 },
issn = { 2249-0868 },
pages = { 17-21 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number44/machine-learning-for-user-authentication-on-mobile-devices-using-typing-patterns/ },
doi = { 10.5120/ijais2024451971 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-05-30T21:54:12.315900+05:30
%A Odeniyi O.A.
%T Machine Learning for User-Authentication on Mobile Devices using Typing Patterns
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 44
%P 17-21
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the rising cases of security issues, it has become a major concern and a matter worthy of note that having a reliable way of ensuring safe activities online or offline is an essential commodity in our present day. It is therefore important to consider safety a priority as individuals use their smartphones and mobile devices since these devices have come to be part of our lives. This research therefore focuses on how what an individual has, that is, how a behavioral pattern which is unique to every individual can be harnessed to ensure security and privacy of data and information. In doing this, Decision Tree (DT) and K-Nearest Neighbor (KNN), both machine learning algorithms were implemented and used to analyze the features of different people’s typing patters. A static password was used and every subject was required to type the password into a smartphone in order to capture their typing features. Afterwards, the required features were extracted and further analyzed for the purpose of use for security. At the end of our experiments, the results came out with an accuracy of 99.12% and 99.92% from KNN and DT respectively.

References
  1. Simon Parkinson, Saad Khan, Alexandru-Mihai Badea, Andrew Crampton, Na Liu and Qing Xu (2022), An empirical analysis of keystroke dynamics in passwords: A longitudinal study, https://doi.org/10.1049/bme2.12087
  2. Tiago Dias, João Vitorino, Eva Maia, Orlando Sousa, Isabel Praça (2023) KeyRecs: A keystroke dynamics and typing pattern recognition dataset, Data in Brief, Volume 50, 2023, 109509, ISSN 2352-3409
  3. Altwaijry, N. (2023) Authentication by Keystroke Dynamics: The Influence of Typing Language. Appl. Sci. 2023, 13, 11478. https://doi.org/10.3390/app132011478
  4. Charbuty Bahzad and Mohsin Abdulazeez Adnan. (2021), Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2. 20-28. 10.38094/jastt20165.
  5. Kohavi Ronny and Quinlan Ross (2002), Data mining tasks and methods: Classification: Decision-tree discovery. Handbook of Data Mining and Knowledge Discovery, 267-276.
  6. Oluwatobi Ayodeji Akanbi, Iraj Sadegh Amiri and Elahe Fazeldehkordi (2015), A Machine-Learning Approach to Phishing Detection and Defense, Syngress, 2015, Pages 35-43, ISBN 9780128029275, https://doi.org/10.1016/B978-0-12-802927-5.00003-4.
  7. Alsammak, Ihab & Sahib, Humam, H.Itwee and Wasan. (2020), An Enhanced Performance of K-Nearest Neighbor (K-NN) Classifier to Meet New Big Data Necessities, IOP Conference Series: Materials Science and Engineering, 928. 032013. 10.1088/1757-899X/928/3/032013.
Index Terms

Computer Science
Information Sciences
Pattern Recognition
Security
Algorithms
Machine Learning

Keywords

Decision Trees K-Nearest Neighbor Recall Accuracy False Positive