Machine Learning as an Enabler of Continuous and Adaptive Authentication in Multimedia Mobile Devices

2020 ◽  
pp. 21-47
Author(s):  
José María Jorquera Valero ◽  
Pedro Miguel Sánchez Sánchez ◽  
Alberto Huertas Celdran ◽  
Gregorio Martínez Pérez
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Machine Learning Techniques ◽  
Security Level ◽  
Design And Implementation ◽  
Continuous Authentication ◽  
Learning Techniques ◽  
Authentication System ◽  
And Performance ◽  
Selection Of

Continuous authentication systems allow users not to possess or remember something to authenticate themselves. These systems perform a permanent authentication that improves the security level of traditional mechanisms, which just authenticate from time to time. Despite the benefits of continuous authentication, the selection of dimensions and characteristics modelling of user's behaviour, and the creation and management of precise models based on Machine learning, are two important open challenges. This chapter proposes a continuous and adaptive authentication system that uses Machine Learning techniques based on the detection of anomalies. Applications usage and the location of the mobile device are considered to detect abnormal behaviours of users when interacting with the device. The proposed system provides adaptability to behavioural changes through the insertion and elimination of patterns. Finally, a proof of concept and several experiments justify the decisions made during the design and implementation of this work, as well as demonstrates its suitability and performance.

scholarly journals Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3769 ◽  
Author(s):  
José Jorquera Valero ◽  
Pedro Sánchez Sánchez ◽  
Lorenzo Fernández Maimó ◽  
Alberto Huertas Celdrán ◽  
Marcos Arjona Fernández ◽  
...  
Keyword(s):  
Mobile Devices ◽  
Quality Of Experience ◽  
Machine Learning Techniques ◽  
Proof Of Concept ◽  
Design And Implementation ◽  
Continuous Authentication ◽  
Learning Techniques ◽  
Authentication System ◽  
Critical Aspects

Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight the enhancement of the system security, having permanently authenticated the users; and the improvement of the users’ quality of experience, minimising the use of authentication credentials. Despite the benefits of these systems, they also have open challenges such as the authentication accuracy and the adaptability to new users’ behaviours. Continuous authentication systems should manage these challenges without forgetting critical aspects of mobile devices such as battery consumption, computational limitations and response time. With the goal of improving these previous challenges, the main contribution of this paper is the design and implementation of an intelligent and adaptive continuous authentication system for mobile devices. The proposed system enables the real-time users’ authentication by considering statistical information from applications, sensors and Machine Learning techniques based on anomaly detection. Several experiments demonstrated the accuracy, adaptability, and resources consumption of our solution. Finally, its utility is validated through the design and implementation of an online bank application as proof of concept, which allows users to perform different actions according to their authentication level.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2012 ◽  
pp. 969-985
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems

2011 ◽  
pp. 121-138
Author(s):  
Floriana Esposito ◽  
Teresa M.A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Machine Learning ◽  
Mobile Device ◽  
Pattern Discovery ◽  
User Interaction ◽  
User Model ◽  
Mobile Systems ◽  
Machine Learning Techniques ◽  
Two Phase ◽  
Learning Techniques ◽  
First Time

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.

scholarly journals Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

2019 ◽  
Vol 119 (3) ◽  
pp. 676-696 ◽  
Author(s):  
Zhongyi Hu ◽  
Raymond Chiong ◽  
Ilung Pranata ◽  
Yukun Bao ◽  
Yuqing Lin
Keyword(s):  
Machine Learning ◽  
Class Imbalance ◽  
Performance Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Real World Data ◽  
Content Type ◽  
Domain Identification ◽  
Learning Techniques ◽  
And Performance

Purpose Malicious web domain identification is of significant importance to the security protection of internet users. With online credibility and performance data, the purpose of this paper to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e. there are more benign web domains than malicious ones). Design/methodology/approach The authors propose an integrated resampling approach to handle class imbalance by combining the synthetic minority oversampling technique (SMOTE) and particle swarm optimisation (PSO), a population-based meta-heuristic algorithm. The authors use the SMOTE for oversampling and PSO for undersampling. Findings By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain data sets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value Online credibility and performance data are applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world data sets with different imbalance ratios.

scholarly journals Design and Implementation of V2V and V2I Communication Systems using ML based Li-Fi technology

2021 ◽  
Author(s):  
Manimegaai C T ◽  
kali muthu ◽  
sabitha gauni
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Machine Learning Techniques ◽  
Light Emitting ◽  
Design And Implementation ◽  
Vehicle To Vehicle ◽  
The People ◽  
Learning Techniques ◽  
Vehicle To Infrastructure ◽  
Vehicle To Infrastructure Communication

Abstract These days population are taking a risk in their drive and in no time dangers are happening, and loosing lives by doing tiny wrongs when on drive near restricted zones. To escape these accidents to make population risk free traffic department are introducing signboards. But then again with the ignorance of the people, dangers are happening again, so “Li-Fi technology” is being used here to decrease the count of accidents. The transmission takes place with the help of LEDs (Light Emitting Diodes).Text, audio and video can also be transmitted with the help of this li-fi. The transmission is done when the light turns on and off. When this is compared to Wi-Fi it has many advantages like this light is not harmful to human body. The Transmission takes place in the form of zeroes and ones. Therefore to avoid accidents we suggested an intelligent, adaptable, and efficient model that utilizes Machine Learning techniques. The proposed system helps in vehicle to vehicle and vehicle to Infrastructure communication systems.

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