Adaptive Continuous Authentication System for Smartphones using Hyper Negative Selection and Random Forest Algorithms

Maryam M Alharbi,Rashiq Rafiq Marie

doi:10.14569/ijacsa.2021.0120859

Maryam M Alharbi, Rashiq Rafiq Marie

Open Access

https://doi.org/10.14569/ijacsa.2021.0120859

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Abstract

As smartphones have become a part of our daily lives, including payment and banking transactions; therefore, increasing current data and privacy protection models is essential. A continuous authentication model aims to track the smartphone user's interaction after the initial login. However, current continuous authentication models are limited due to dynamic changes in smartphone user behavior. This paper aims to enhance smartphone user privacy and security using continuous authentication based on touch dynamics by proposing a framework for smartphone devices based on user touch behavior to provide a more accurate and adaptive learning model. We adopt a hybrid model based on the Hyper Negative Selection Algorithm (HNSA) as an artificial immune system (AIS) and the random forest ensemble classifier to instantly classify a user behavior. With the new approach, a decision model could detect normal/abnormal user behavior and update a user profile continuously while using his/her smartphone. The proposed approach was compared with the v-detector and HNSA, where it shows a high average accuracy of 98.5%, a low false alarm rate, and an increased detection rate. The new model is significant as it could be integrated with a smartphone to increase user privacy instantly. It is concluded that the proposed approach is efficient and valuable for smartphone users to increase their privacy while dynamic user behaviors evolve to change.

Highlights

According to the Global System for Mobile Communications Association (GSMA) Intelligence Reports, there are more than 5 billion smartphone users in the world today [1]
This paper aims to develop a model for continuous authentication (CA) using Hyper Negative Selection Algorithm (HNSA) and the random forest ensemble classifier to authenticate and detect illegal smartphone users
The proposed model Random Forest Negative Selection Algorithm (RFNSA) would be adopted in security fields in continuous authentication systems in smartphones

Summary

Introduction

According to the Global System for Mobile Communications Association (GSMA) Intelligence Reports, there are more than 5 billion smartphone users in the world today [1]. The entry-point authentication models include PIN authentications, passwords, or biometrics; these models are considered discontinuous because they do not track smartphone access while interacting with the system. Continuous authentication keeps a consistent track of a smartphone's access over time. This study classifies authentication systems into continuous or discontinuous models based on how they track user interaction during smartphone usage. Knowledge-based models depend on patterns that a mobile owner knows, such as passwords (such as a PIN or passcode). Regardless of which category is used, the passcode of possession-based models is considered an entry-point authentication method as it does not follow the user's actions while the user is not actively using the smartphone. The different authentication techniques could be used in smartphones, namely, slide lock, number lock, graphical-based passwords, fingerprint, and face recognition authentications [8]

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