Mobile behaviometric framework for sociability assessment and identification of smartphone users

Anjomshoa, Fazel; Catalfamo, Matthew; Hecker, Daniel E.; Helgeland, Nicklaus; Rasch, Andrew; Kantarcı, Burak; Erol‐Kantarci, Melike; Schuckers, Stephanie

doi:10.1109/iscc.2016.7543880

Cited by 15 publications

(8 citation statements)

References 13 publications

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“…Social network usage is increasing at a rapid rate through the use of a smartphone, leading to utilize them in continuous authentication. TrackMasion is a behaviometric analytics platform to monitor social network usage to identify user behavior and utilize it in mobile authentication (Anjomshoa et al, 2016). Further, Radial Basis Function Neural Network applied on the short messaging service to create a linguistic profile that can be used to determine the user behavior and perform continuous authentication (Saevanee et al, 2011).…”

Section: User Behavior Learning Methodsmentioning

confidence: 99%

Application of Machine Learning to User Behavior-Based Authentication in Smartphone and Web

Jayabalan

2022

Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity

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Authentication is the preliminary security mechanism employed in the information system to identify the legitimacy of the user. With technological advancements, hackers with sophisticated techniques easily crack single-factor authentication (username and password). Therefore, organizations started to deploy multi-factor authentication (MFA) to increase the complexity of the access to the system. Despite the MFA increasing the security of the digital service, the usable security should be given equal importance. The user behavior-based authentication provides a means to analyze the user interaction with the system in a non-intrusive way to identify the user legitimacy. This chapter presents a review of user behavior-based authentication in smartphones and websites. Moreover, the review highlights some of the common features, techniques, and evaluation criteria usually considered in the development of user behavior profiling.

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Section: User Behavior Learning Methodsmentioning

confidence: 99%

Application of Machine Learning to User Behavior-Based Authentication in Smartphone and Web

Jayabalan

2022

Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity

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“…Websites and social software, e.g., Facebook and Twitter, comprise huge amounts of data to improve the service quality of the social network. In [17] [18], TrackMaison keeps track of social network service usage of smartphone users through data usage, location, usage frequency and session duration for identifying users' social behaviour.…”

Section: Introductionmentioning

confidence: 99%

On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision

Sun

Ding

Shu

et al. 2022

IEEE Systems Journal

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Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges that are required to be considered. Mobile Crowd Sensing (MCS) embeds three beneficial characteristics: a) cost-effectiveness, b) scalability, and c) mobility and robustness. With the Internet of Things (IoT) becoming a reality, the smart phones are widely becoming available even in remote areas. Hence, both the MCSs characteristics and the plug and play widely available infrastructure provides huge opportunities for the MCS-enabled smart agriculture.opening up several new opportunities at the application level. In this paper, we extensively evaluate the Agriculture Mobile Crowd Sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we provide a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low cost requirements. However, we note that AMCSs may still posses limitations in regard to data integrity and quality to be considered as a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns the MCS-enabled agriculture by putting forward six potential applications of AMCS-enabled agriculture. Finally, we propose future research and focus on agricultural characteristics, e.g., seasonality and regionality.

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“…Others object-to-human techniques to continuously identify persons leverage the way people interact with their smartphones and the available applications. Most users have regular behavioral patterns that can be modeled and exploited for continuous recognition of behavioral signatures [13,14]. Therefore, behavioral characteristics of mobile users can allow continuous authentication of a user on a personal device.…”

Section: Introductionmentioning

confidence: 99%

H2O: Secure Interactions in IoT via Behavioral Fingerprinting

2021

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Sharing data and services in the Internet of Things (IoT) can give rise to significant security concerns with information being sensitive and vulnerable to attacks. In such an environment, objects can be either public resources or owned by humans. For this reason, the need of monitoring the reliability of all involved actors, both persons and smart objects, assuring that they really are who they claim to be, is becoming an essential property of the IoT, with the increase in the pervasive adoption of such a paradigm. In this paper, we tackle this problem by proposing a new framework, called H2O (Human to Object). Our solution is able to continuously authenticate an entity in the network, providing a reliability assessment mechanism based on behavioral fingerprinting. A detailed security analysis evaluates the robustness of the proposed protocol; furthermore, a performance analysis shows the feasibility of our approach.

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Mobile behaviometric framework for sociability assessment and identification of smartphone users

Cited by 15 publications

References 13 publications

Application of Machine Learning to User Behavior-Based Authentication in Smartphone and Web

Application of Machine Learning to User Behavior-Based Authentication in Smartphone and Web

On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision

H2O: Secure Interactions in IoT via Behavioral Fingerprinting

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