A flexible authentication scheme for smart home networks using app interactions and machine learning

2020 ◽  
Vol 39 (5) ◽  
pp. 6009-6020
Author(s):  
Yosef Ashibani ◽  
Qusay H. Mahmoud
Keyword(s):  
Machine Learning ◽  
Smart Home ◽  
User Authentication ◽  
Authentication Scheme ◽  
Home Networks ◽  
Flexible Machine ◽  
Continuous Authentication ◽  
Continuous User ◽  
Access Patterns ◽  
Application Access

Smartphones have now become ubiquitous for accessing and controlling home appliances in smart homes, a popular application of the Internet of Things. User authentication on smartphones is mostly achieved at initial access. However, without applying a continuous authentication process, the network will be susceptible to unauthorized users. This issue emphasizes the importance of offering a continuous authentication scheme to identify the current user of the device. This can be achieved by extracting information during smartphone usage, including application access patterns. In this paper, we present a flexible machine learning user authentication scheme for smart home networks based on smartphone usage. Considering that users may run their smartphone applications differently during different day time intervals as well as different days of the week, new features are extracted by considering this information. The scheme is evaluated on a real-world dataset for continuous user authentication. The results show that the presented scheme authenticates users with high accuracy.

Continuous User Authentication on Touchscreen Using Behavioral Biometrics Utilizing Machine Learning Approaches

2020 ◽  
pp. 243-281 ◽  
Author(s):  
Amany Sarhan ◽  
Ahmed Ramadan
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Features Selection ◽  
Learning Approaches ◽  
Touch Input ◽  
Continuous Authentication ◽  
Classification Framework ◽  
Continuous User ◽  
Level Model ◽  
High Level

Nowadays, touchscreen mobile devices make up a larger share in the market, necessitating effective and robust methods to continuously authenticate touch-based device users. A classification framework is proposed that learns the touch behavior of a user and is able afterwards to authenticate users by monitoring their behavior in performing input touch actions. Two models of features are built; the low-level features (stoke-level) model or the high-level abstracted features (session-level) model. In building these models, two different methods for features selection and data classification were weighted features and PCA. Two classification algorithms were used; ANN and SVM. The experimental results indicate the possibility of continuous authentication for touch-input users with higher promises for session-level features than stroke-level features. Authors found out that using weighted features method and artificial neural networks in building the session-level model yields the most efficient and accurate behavioral biometric continuous user authentication.

A Robust Two-Factor User Authentication Scheme-Based ECC for Smart Home in IoT

2021 ◽  
pp. 1-12
Author(s):  
Shihong Zou ◽  
Qiang Cao ◽  
Chenyu Wang ◽  
Zifu Huang ◽  
Guoai Xu
Keyword(s):  
Smart Home ◽  
User Authentication ◽  
Authentication Scheme ◽  
User Authentication Scheme

Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines

2011 ◽  
pp. 232-252
Author(s):  
Sérgio Roberto de Lima e Silva Filho ◽  
Mauro Roisenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
User Authentication ◽  
Keystroke Dynamics ◽  
Continuous Authentication ◽  
Biometric Characteristic ◽  
Continuous User ◽  
Computer Keyboard

This chapter proposes an authentication methodology that is both inexpensive and non-intrusive and authenticates users continuously while using a computer keyboard. This proposed methodology uses neural network committee machines. The committee consists of several independent neural networks trained to recognize a behavioral biometric characteristic: user’s typing pattern. Continuous authentication prevents potential attacks when users leave their desks without logging out or locking their computer session. Some experiments were conducted to evaluate and to calibrate the authentication committee. Best results show that a 0% FAR and a 0.15% FRR can be achieved when different thresholds are used in the system for each user. In this proposed methodology, capture system does not need to concern about typing errors in the text. Another feature of this methodology is that new users can be easily added to the system, with no need to re-train all neural networks involved.

A Lightweight Privacy-Aware Continuous Authentication Protocol-PACA

2021 ◽  
Vol 24 (4) ◽  
pp. 1-28
Author(s):  
Abbas Acar ◽  
Shoukat Ali ◽  
Koray Karabina ◽  
Cengiz Kaygusuz ◽  
Hidayet Aksu ◽  
...  
Keyword(s):  
Template Matching ◽  
User Authentication ◽  
Third Party ◽  
Biometric Data ◽  
Concrete System ◽  
Current State ◽  
Continuous Authentication ◽  
User Data ◽  
Continuous User ◽  
Privacy Issues

As many vulnerabilities of one-time authentication systems have already been uncovered, there is a growing need and trend to adopt continuous authentication systems. Biometrics provides an excellent means for periodic verification of the authenticated users without breaking the continuity of a session. Nevertheless, as attacks to computing systems increase, biometric systems demand more user information in their operations, yielding privacy issues for users in biometric-based continuous authentication systems. However, the current state-of-the-art privacy technologies are not viable or costly for the continuous authentication systems, which require periodic real-time verification. In this article, we introduce a novel, lightweight, <underline>p</underline>rivacy-<underline>a</underline>ware, and secure <underline>c</underline>ontinuous <underline>a</underline>uthentication protocol called PACA. PACA is initiated through a password-based key exchange (PAKE) mechanism, and it continuously authenticates users based on their biometrics in a privacy-aware manner. Then, we design an actual continuous user authentication system under the proposed protocol. In this concrete system, we utilize a privacy-aware template matching technique and a wearable-assisted keystroke dynamics-based continuous authentication method. This provides privacy guarantees without relying on any trusted third party while allowing the comparison of noisy user inputs (due to biometric data) and yielding an efficient and lightweight protocol. Finally, we implement our system on an Apple smartwatch and perform experiments with real user data to evaluate the accuracy and resource consumption of our concrete system.

scholarly journals Authentication protocols for smart homes

2021 ◽  
Author(s):  
Maninder Singh Raniyal
Keyword(s):  
Smart Home ◽  
User Authentication ◽  
Mutual Authentication ◽  
Smart Homes ◽  
Authentication Scheme ◽  
Security Requirements ◽  
Iot Security ◽  
Communication Topology ◽  
User Authentication Scheme ◽  
Authentication Schemes

One of the IoT's greatest opportunity and application still lies ahead in the form of smart home. In this ubiquitous/automated environment, due to the most likely heterogeneity of objects, communication, topology, security protocols, and the computationally limited na- ture of IoT objects, conventional authentication schemes may not comply with IoT security requirements since they are considered impractical, weak, or outdated. This thesis proposes: (1) The design of a two-factor device-to-device (D2D) Mutual Authentication Scheme for Smart Homes using OTP over Infrared Channel (referred to as D2DA-OTP-IC scheme); (2) The design of two proxy-password protected OTP-based schemes for smart homes, namely, the Password Protected Inter-device OTP-based Authentication scheme over Infrared Chan- nel and the Password Protected Inter-device OTP-based Authentication scheme using public key infrastructure; and (3) The design of a RSA-based two-factor user Authentication scheme for Smart Home using Smart Card.

Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines

2013 ◽  
pp. 410-429
Author(s):  
Sérgio Roberto de Lima e Silva Filho ◽  
Mauro Roisenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
User Authentication ◽  
Keystroke Dynamics ◽  
Continuous Authentication ◽  
Biometric Characteristic ◽  
Continuous User ◽  
Computer Keyboard

This chapter proposes an authentication methodology that is both inexpensive and non-intrusive and authenticates users continuously while using a computer keyboard. This proposed methodology uses neural network committee machines. The committee consists of several independent neural networks trained to recognize a behavioral biometric characteristic: user’s typing pattern. Continuous authentication prevents potential attacks when users leave their desks without logging out or locking their computer session. Some experiments were conducted to evaluate and to calibrate the authentication committee. Best results show that a 0% FAR and a 0.15% FRR can be achieved when different thresholds are used in the system for each user. In this proposed methodology, capture system does not need to concern about typing errors in the text. Another feature of this methodology is that new users can be easily added to the system, with no need to re-train all neural networks involved.

Secure remote anonymous user authentication scheme for smart home environment

2020 ◽  
Vol 9 ◽  
pp. 100158 ◽  
Author(s):  
Moneer Fakroon ◽  
Mohammed Alshahrani ◽  
Fayez Gebali ◽  
Issa Traore
Keyword(s):  
Home Environment ◽  
Smart Home ◽  
User Authentication ◽  
Authentication Scheme ◽  
Smart Home Environment ◽  
User Authentication Scheme
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